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Tadepalli K, Das A, Meena T, Roy S. Bridging gaps in artificial intelligence adoption for maternal-fetal and obstetric care: Unveiling transformative capabilities and challenges. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025; 263:108682. [PMID: 40023965 DOI: 10.1016/j.cmpb.2025.108682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 02/04/2025] [Accepted: 02/18/2025] [Indexed: 03/04/2025]
Abstract
PURPOSE This review aims to comprehensively explore the application of Artificial Intelligence (AI) to an area that has not been traditionally explored in depth: the continuum of maternal-fetal health. In doing so, the intent was to examine this physiologically continuous spectrum of mother and child health, as well as to highlight potential pitfalls, and suggest solutions for the same. METHOD A systematic search identified studies employing AI techniques for prediction, diagnosis, and decision support employing various modalities like imaging, electrophysiological signals and electronic health records in the domain of obstetrics and fetal health. In the selected articles then, AI applications in fetal morphology, gestational age assessment, congenital defect detection, fetal monitoring, placental analysis, and maternal physiological monitoring were critically examined both from the perspective of the domain and artificial intelligence. RESULT AI-driven solutions demonstrate promising capabilities in medical diagnostics and risk prediction, offering automation, improved accuracy, and the potential for personalized medicine. However, challenges regarding data availability, algorithmic transparency, and ethical considerations must be overcome to ensure responsible and effective clinical implementation. These challenges must be urgently addressed to ensure a domain as critical to public health as obstetrics and fetal health, is able to fully benefit from the gigantic strides made in the field of artificial intelligence. CONCLUSION Open access to relevant datasets is crucial for equitable progress in this critical public health domain. Integrating responsible and explainable AI, while addressing ethical considerations, is essential to maximize the public health benefits of AI-driven solutions in maternal-fetal care.
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Affiliation(s)
- Kalyan Tadepalli
- Sir HN Reliance Foundation Hospital, Girgaon, Mumbai, 400004, India; Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai, 410206, India
| | - Abhijit Das
- Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai, 410206, India
| | - Tanushree Meena
- Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai, 410206, India
| | - Sudipta Roy
- Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai, 410206, India.
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Pugliese N, Bertazzoni A, Hassan C, Schattenberg JM, Aghemo A. Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care. Cancers (Basel) 2025; 17:722. [PMID: 40075570 PMCID: PMC11899536 DOI: 10.3390/cancers17050722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2025] [Revised: 02/08/2025] [Accepted: 02/17/2025] [Indexed: 03/14/2025] Open
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a leading cause of chronic liver disease. In recent years, artificial intelligence (AI) has attracted significant attention in healthcare, particularly in diagnostics, patient management, and drug development, demonstrating immense potential for application and implementation. In the field of MASLD, substantial research has explored the application of AI in various areas, including patient counseling, improved patient stratification, enhanced diagnostic accuracy, drug development, and prognosis prediction. However, the integration of AI in hepatology is not without challenges. Key issues include data management and privacy, algorithmic bias, and the risk of AI-generated inaccuracies, commonly referred to as "hallucinations". This review aims to provide a comprehensive overview of the applications of AI in hepatology, with a focus on MASLD, highlighting both its transformative potential and its inherent limitations.
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Affiliation(s)
- Nicola Pugliese
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, MI, Italy; (N.P.); (A.B.); (C.H.)
- Division of Internal Medicine and Hepatology, Department of Gastroenterology, IRCCS Humanitas Research Hospital, 20089 Rozzano, MI, Italy
| | - Arianna Bertazzoni
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, MI, Italy; (N.P.); (A.B.); (C.H.)
- Division of Internal Medicine and Hepatology, Department of Gastroenterology, IRCCS Humanitas Research Hospital, 20089 Rozzano, MI, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, MI, Italy; (N.P.); (A.B.); (C.H.)
- Endoscopy Unit, Department of Gastroenterology, IRCCS Humanitas Research Hospital, 20089 Rozzano, MI, Italy
| | - Jörn M. Schattenberg
- Department of Internal Medicine II, Saarland University Medical Center, 66421 Homburg, Germany;
| | - Alessio Aghemo
- Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, MI, Italy; (N.P.); (A.B.); (C.H.)
- Division of Internal Medicine and Hepatology, Department of Gastroenterology, IRCCS Humanitas Research Hospital, 20089 Rozzano, MI, Italy
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3
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Kaffas AE, Bhatraju KC, Vo-Phamhi JM, Tiyarattanachai T, Antil N, Negrete LM, Kamaya A, Shen L. Development of a Deep Learning Model for Classification of Hepatic Steatosis from Clinical Standard Ultrasound. ULTRASOUND IN MEDICINE & BIOLOGY 2025; 51:242-249. [PMID: 39537545 DOI: 10.1016/j.ultrasmedbio.2024.09.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 09/26/2024] [Accepted: 09/29/2024] [Indexed: 11/16/2024]
Abstract
OBJECTIVE Early detection and monitoring of hepatic steatosis can help establish appropriate preventative measures against progression to more advanced disease. We aimed to develop a deep learning (DL) program for classification of hepatic steatosis from standard-of-care grayscale ultrasound (US) images. METHODS In this single-center retrospective study, we utilized grayscale US images from January 1, 2010, to October 23, 2022, labeled with magnetic resonance imaging (MRI) proton density fat fraction (MRI-PDFF) to develop a DL multi-instance program for differentiating normal (S0) from steatotic liver (S1/2/3) and normal/mild steatosis (S0/1) from moderate/severe steatosis (S2/3). Diagnostic performances were assessed with area under the receiver operating characteristic curves (AUC), sensitivity, specificity and balanced accuracy with 95% confidence interval (CI). RESULTS A total of 403 patients with 403 US exams were included: 171 (42%) were normal (S0: MRI-PDFF <5%), 154 (38%) had mild steatosis (S1: MRI-PDFF 5-17.4%), 29 (7%) had moderate steatosis (S2: MRI-PDFF >17.4%-22.1%) and 49 (12%) had severe steatosis (S3: MRI-PDFF >22.1%). The dataset was split to include 322 patients in train/validation and 81 patients in a holdout test set (kept blind). The S0 versus S1/2/3 model achieved 81.3% (95% CI 72.1-90.5) AUC, 81.1% (70.6-91.6) sensitivity, 71.4% (54.7-88.2) specificity and 76.3% (66.4-86.2) balanced accuracy. The S0/1 versus S2/3 model achieved 95.9% (89-100) AUC, 87.5% (71.3-100) sensitivity, 96.9% (92.7-100) specificity and 92.2% (83.8-100) balanced accuracy. A multi-class model achieved a sensitivity of 71.4% (54.7-88.2) for S0, 67.6% (52.5-82.7) for S1 and 87.5% (71.3-100) for S2/3; specificity for the same model was 81.1% (70.6-91.6) for S0, 77.3% (64.9-89.7) for S1 and 96.9% (92.7-100) for S2/3. CONCLUSION Our DL program offered high sensitivity and accuracy in detecting and categorizing hepatic steatosis from standard-of-care ultrasound.
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Affiliation(s)
- Ahmed El Kaffas
- Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Krishna Chaitanya Bhatraju
- Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jenny M Vo-Phamhi
- Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Thodsawit Tiyarattanachai
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Neha Antil
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Lindsey M Negrete
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Aya Kamaya
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Luyao Shen
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
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Drazinos P, Gatos I, Katsakiori PF, Tsantis S, Syrmas E, Spiliopoulos S, Karnabatidis D, Theotokas I, Zoumpoulis P, Hazle JD, Kagadis GC. Comparison of deep learning schemes in grading non-alcoholic fatty liver disease using B-mode ultrasound hepatorenal window images with liver biopsy as the gold standard. Phys Med 2025; 129:104862. [PMID: 39626614 DOI: 10.1016/j.ejmp.2024.104862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Revised: 10/11/2024] [Accepted: 11/27/2024] [Indexed: 01/07/2025] Open
Abstract
BACKGROUND/INTRODUCTION To evaluate the performance of pre-trained deep learning schemes (DLS) in hepatic steatosis (HS) grading of Non-Alcoholic Fatty Liver Disease (NAFLD) patients, using as input B-mode US images containing right kidney (RK) cortex and liver parenchyma (LP) areas indicated by an expert radiologist. METHODS A total of 112 consecutively enrolled, biopsy-validated NAFLD patients underwent a regular abdominal B-mode US examination. For each patient, a radiologist obtained a B-mode US image containing RK cortex and LP and marked a point between the RK and LP, around which a window was automatically cropped. The cropped image dataset was augmented using up-sampling, and the augmented and non-augmented datasets were sorted by HS grade. Each dataset was split into training (70%) and testing (30%), and fed separately as input to InceptionV3, MobileNetV2, ResNet50, DenseNet201, and NASNetMobile pre-trained DLS. A receiver operating characteristic (ROC) analysis of hepatorenal index (HRI) measurements by the radiologist from the same cropped images was used for comparison with the performance of the DLS. RESULTS With the test data, the DLS reached 89.15 %-93.75 % accuracy when comparing HS grades S0-S1 vs. S2-S3 and 79.69 %-91.21 % accuracy for S0 vs. S1 vs. S2 vs. S3 with augmentation, and 80.45-82.73 % accuracy when comparing S0-S1 vs. S2-S3 and 59.54 %-63.64 % accuracy for S0 vs. S1 vs. S2 vs. S3 without augmentation. The performance of radiologists' HRI measurement after ROC analysis was 82 %, 91.56 %, and 96.19 % for thresholds of S ≥ S1, S ≥ S2, and S = S3, respectively. CONCLUSION All networks achieved high performance in HS assessment. DenseNet201 with the use of augmented data seems to be the most efficient supplementary tool for NAFLD diagnosis and grading.
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Affiliation(s)
- Petros Drazinos
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece; Diagnostic Echotomography SA, Kifissia, GR 14561, Greece
| | - Ilias Gatos
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece
| | - Paraskevi F Katsakiori
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece
| | - Stavros Tsantis
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece
| | - Efstratios Syrmas
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece
| | - Stavros Spiliopoulos
- Second Department of Radiology, School of Medicine, University of Athens, Athens, GR 12461, Greece
| | - Dimitris Karnabatidis
- Department of Radiology, School of Medicine, University of Patras, Patras, GR 26504, Greece
| | | | | | - John D Hazle
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - George C Kagadis
- 3DMI Research Group, Department of Medical Physics, School of Medicine, University of Patras, Rion, GR 26504, Greece; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
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Guo L, Shi L, Wang W, Wang X. Neural Network Classification Algorithm Based on Self-attention Mechanism and Ensemble Learning for MASLD Ultrasound Images. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:1361-1371. [PMID: 38910034 DOI: 10.1016/j.ultrasmedbio.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 04/11/2024] [Accepted: 05/10/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Ultrasound image examination has become the preferred choice for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD) due to its non-invasive nature. Computer-aided diagnosis (CAD) technology can assist doctors in avoiding deviations in the detection and classification of MASLD. METHOD We propose a hybrid model that integrates the pre-trained VGG16 network with an attention mechanism and a stacking ensemble learning model, which is capable of multi-scale feature aggregation based on the self-attention mechanism and multi-classification model fusion (Logistic regression, random forest, support vector machine) based on stacking ensemble learning. The proposed hybrid method achieves four classifications of normal, mild, moderate, and severe fatty liver based on ultrasound images. RESULT AND CONCLUSION Our proposed hybrid model reaches an accuracy of 91.34% and exhibits superior robustness against interference, which is better than traditional neural network algorithms. Experimental results show that, compared with the pre-trained VGG16 model, adding the self-attention mechanism improves the accuracy by 3.02%. Using the stacking ensemble learning model as a classifier further increases the accuracy to 91.34%, exceeding any single classifier such as LR (89.86%) and SVM (90.34%) and RF (90.73%). The proposed hybrid method can effectively improve the efficiency and accuracy of MASLD ultrasound image detection.
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Affiliation(s)
- Lijuan Guo
- Children's Hospital of Shanxi & Women Health Center of Shanxi, Taiyuan, China.
| | - Liling Shi
- Children's Hospital of Shanxi & Women Health Center of Shanxi, Taiyuan, China.
| | - Wenjuan Wang
- Shanxi International Travel Health Care Center, Taiyuan, China
| | - Xiaotong Wang
- Children's Hospital of Shanxi & Women Health Center of Shanxi, Taiyuan, China
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Liu Y, Yu W, Wang P, Huang Y, Li J, Li P. Deep Learning With Ultrasound Images Enhance the Diagnosis of Nonalcoholic Fatty Liver. ULTRASOUND IN MEDICINE & BIOLOGY 2024:S0301-5629(24)00291-6. [PMID: 39179453 DOI: 10.1016/j.ultrasmedbio.2024.07.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/21/2024] [Accepted: 07/29/2024] [Indexed: 08/26/2024]
Abstract
OBJECTIVE This research aimed to improve diagnosis of non-alcoholic fatty liver disease (NAFLD) by deep learning with ultrasound Images and reduce the impact of the professional competence and personal bias of the diagnostician. METHOD Three convolutional neural network models were used to classify and identify the ultrasound images to obtain the best network. Then, the features in the ultrasound images were extracted and a new convolutional neural network was created based on the best network. Finally, the accuracy of several networks was compared and the best network was evaluated using AUC. RESULTS Models of VGG16, ResNet50, and Inception-v3 were individually applied to classify and identify 710 ultrasound images containing NAFLD, demonstrating accuracies of 66.2%, 58.5%, and 59.2%, respectively. To further improve the classification accuracy, two features are presented: the ultrasound echo attenuation coefficient (θ), derived from fitting brightness values within sliding region of interest (ROIs), and the ratio of Doppler effect (ROD), identified through analyzing spots exhibiting the Doppler effect. Then, a multi-input deep learning network framework based on the VGG16 model is established, where the VGG16 model processes ultrasound image, while the fully connected layers handle θ and ROD. Ultimately, these components are combined to jointly generate predictions, demonstrating robust diagnostic capabilities for moderate to severe fatty liver (AUC = 0.95). Moreover, the average accuracy is increased from 64.8% to 77.5%, attributed to the introduction of two advanced features with domain knowledge. CONCLUSION This research holds significant potential in aiding doctors for more precise and efficient diagnosis of ultrasound images related to NAFLD.
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Affiliation(s)
- Yao Liu
- Department of Ultrasound, The Second Affliated Hospital of Chongqing Medical University, Chongqing, China; Ultrasonography Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Wenrou Yu
- College of Physics, Chongqing University, Chongqing, China
| | - Peizheng Wang
- College of Physics, Chongqing University, Chongqing, China; School of Software Technology, Zhejiang University, Ningbo, China
| | - Yingzhou Huang
- College of Physics, Chongqing University, Chongqing, China
| | - Jin Li
- College of Physics, Chongqing University, Chongqing, China
| | - Pan Li
- Department of Ultrasound, The Second Affliated Hospital of Chongqing Medical University, Chongqing, China.
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Qi Y, Vianna P, Cadrin-Chênevert A, Blanchet K, Montagnon E, Belilovsky E, Wolf G, Mullie LA, Cloutier G, Chassé M, Tang A. Simulating federated learning for steatosis detection using ultrasound images. Sci Rep 2024; 14:13253. [PMID: 38858500 PMCID: PMC11164945 DOI: 10.1038/s41598-024-63969-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 06/04/2024] [Indexed: 06/12/2024] Open
Abstract
We aimed to implement four data partitioning strategies evaluated with four federated learning (FL) algorithms and investigate the impact of data distribution on FL model performance in detecting steatosis using B-mode US images. A private dataset (153 patients; 1530 images) and a public dataset (55 patient; 550 images) were included in this retrospective study. The datasets contained patients with metabolic dysfunction-associated fatty liver disease (MAFLD) with biopsy-proven steatosis grades and control individuals without steatosis. We employed four data partitioning strategies to simulate FL scenarios and we assessed four FL algorithms. We investigated the impact of class imbalance and the mismatch between the global and local data distributions on the learning outcome. Classification performance was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. AUCs were 0.93 (95% CI 0.92, 0.94) for source-based partitioning scenario with FedAvg, 0.90 (95% CI 0.89, 0.91) for a centralized model, and 0.83 (95% CI 0.81, 0.85) for a model trained in a single-center scenario. When data was perfectly balanced on the global level and each site had an identical data distribution, the model yielded an AUC of 0.90 (95% CI 0.88, 0.92). When each site contained data exclusively from one single class, irrespective of the global data distribution, the AUC fell in the range of 0.34-0.70. FL applied to B-mode US images provide performance comparable to a centralized model and higher than single-center scenario. Global data imbalance and local data heterogeneity influenced the learning outcome.
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Affiliation(s)
- Yue Qi
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | - Pedro Vianna
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Institute of Biomedical Engineering, Université de Montréal, Montréal, QC, Canada
- Laboratory of Biorheology and Medical Ultrasonics - CRCHUM, Montréal, QC, Canada
| | - Alexandre Cadrin-Chênevert
- Radiology, Radiation Oncology and Nuclear Medicine Department, Université de Montréal, Montréal, QC, Canada
- CISSS de Lanaudière, Joliette, QC, Canada
| | - Katleen Blanchet
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | - Emmanuel Montagnon
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Clinical Laboratory of Image Processing - CRCHUM, Montréal, QC, Canada
| | - Eugene Belilovsky
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
- Concordia University, Montréal, QC, Canada
| | - Guy Wolf
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
- Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
| | - Louis-Antoine Mullie
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
- Department of Medicine, Division of Critical Care Medicine, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Guy Cloutier
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Institute of Biomedical Engineering, Université de Montréal, Montréal, QC, Canada
- Laboratory of Biorheology and Medical Ultrasonics - CRCHUM, Montréal, QC, Canada
- Radiology, Radiation Oncology and Nuclear Medicine Department, Université de Montréal, Montréal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Michaël Chassé
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Department of Medicine, Division of Critical Care Medicine, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - An Tang
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada.
- Clinical Laboratory of Image Processing - CRCHUM, Montréal, QC, Canada.
- Faculty of Medicine, Université de Montréal, Montréal, QC, Canada.
- Département de Radiologie, Centre Hospitalier de l'Université de Montréal (CHUM), 1058 Rue Saint-Denis, Montréal, QC, H2X 3J4, Canada.
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Yen TJ, Yang CT, Lee YJ, Chen CH, Yang HC. Fatty liver classification via risk controlled neural networks trained on grouped ultrasound image data. Sci Rep 2024; 14:7345. [PMID: 38538649 PMCID: PMC10973492 DOI: 10.1038/s41598-024-57386-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/18/2024] [Indexed: 01/03/2025] Open
Abstract
Ultrasound imaging is a widely used technique for fatty liver diagnosis as it is practically affordable and can be quickly deployed by using suitable devices. When it is applied to a patient, multiple images of the targeted tissues are produced. We propose a machine learning model for fatty liver diagnosis from multiple ultrasound images. The machine learning model extracts features of the ultrasound images by using a pre-trained image encoder. It further produces a summary embedding on these features by using a graph neural network. The summary embedding is used as input for a classifier on fatty liver diagnosis. We train the machine learning model on a ultrasound image dataset collected by Taiwan Biobank. We also carry out risk control on the machine learning model using conformal prediction. Under the risk control procedure, the classifier can improve the results with high probabilistic guarantees.
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Affiliation(s)
- Tso-Jung Yen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
| | - Chih-Ting Yang
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Yi-Ju Lee
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Chun-Houh Chen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Hsin-Chou Yang
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
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Lu F, Meng Y, Song X, Li X, Liu Z, Gu C, Zheng X, Jing Y, Cai W, Pinyopornpanish K, Mancuso A, Romeiro FG, Méndez-Sánchez N, Qi X. Artificial Intelligence in Liver Diseases: Recent Advances. Adv Ther 2024; 41:967-990. [PMID: 38286960 DOI: 10.1007/s12325-024-02781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/03/2024] [Indexed: 01/31/2024]
Abstract
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
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Affiliation(s)
- Feifei Lu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
| | - Yao Meng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaoting Song
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, Dalian Medical University, Dalian, China
| | - Xiaotong Li
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Zhuang Liu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Chunru Gu
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Xiaojie Zheng
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China
- Postgraduate College, China Medical University, Shenyang, China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd., Shenyang, China
| | - Kanokwan Pinyopornpanish
- Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Andrea Mancuso
- Medicina Interna 1, Azienda di Rilievo Nazionale Ad Alta Specializzazione Civico-Di Cristina-Benfratelli, Palermo, Italy.
| | | | - Nahum Méndez-Sánchez
- Liver Research Unit, Medica Sur Clinic and Foundation, National Autonomous University of Mexico, Mexico City, Mexico.
| | - Xingshun Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Liver Cirrhosis Study Group, Department of Gastroenterology, General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang, 110840, Liaoning Province, China.
- Postgraduate College, Dalian Medical University, Dalian, China.
- Postgraduate College, China Medical University, Shenyang, China.
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Zamanian H, Shalbaf A, Zali MR, Khalaj AR, Dehghan P, Tabesh M, Hatami B, Alizadehsani R, Tan RS, Acharya UR. Application of artificial intelligence techniques for non-alcoholic fatty liver disease diagnosis: A systematic review (2005-2023). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:107932. [PMID: 38008040 DOI: 10.1016/j.cmpb.2023.107932] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/28/2023]
Abstract
BACKGROUND AND OBJECTIVES Non-alcoholic fatty liver disease (NAFLD) is a common liver disease with a rapidly growing incidence worldwide. For prognostication and therapeutic decisions, it is important to distinguish the pathological stages of NAFLD: steatosis, steatohepatitis, and liver fibrosis, which are definitively diagnosed on invasive biopsy. Non-invasive ultrasound (US) imaging, including US elastography technique, and clinical parameters can be used to diagnose and grade NAFLD and its complications. Artificial intelligence (AI) is increasingly being harnessed for developing NAFLD diagnostic models based on clinical, biomarker, or imaging data. In this work, we systemically reviewed the literature for AI-enabled NAFLD diagnostic models based on US (including elastography) and clinical (including serological) data. METHODS We performed a comprehensive search on Google Scholar, Scopus, and PubMed search engines for articles published between January 2005 and June 2023 related to AI models for NAFLD diagnosis based on US and/or clinical parameters using the following search terms: "non-alcoholic fatty liver disease", "non-alcoholic steatohepatitis", "deep learning", "machine learning", "artificial intelligence", "ultrasound imaging", "sonography", "clinical information". RESULTS We reviewed 64 published models that used either US (including elastography) or clinical data input to detect the presence of NAFLD, non-alcoholic steatohepatitis, and/or fibrosis, and in some cases, the severity of steatosis, inflammation, and/or fibrosis as well. The performances of the published models were summarized, and stratified by data input and algorithms used, which could be broadly divided into machine and deep learning approaches. CONCLUSION AI models based on US imaging and clinical data can reliably detect NAFLD and its complications, thereby reducing diagnostic costs and the need for invasive liver biopsy. The models offer advantages of efficiency, accuracy, and accessibility, and serve as virtual assistants for specialists to accelerate disease diagnosis and reduce treatment costs for patients and healthcare systems.
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Affiliation(s)
- H Zamanian
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - A Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - M R Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - A R Khalaj
- Tehran obesity treatment center, Department of Surgery, Faculty of Medicine, Shahed University, Tehran, Iran
| | - P Dehghan
- Department of Radiology, Imaging Department, Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - M Tabesh
- Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research, Tehran University of Medical Sciences, Tehran, Iran
| | - B Hatami
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - R Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC, Australia
| | - Ru-San Tan
- National Heart Centre Singapore, Singapore 169609, Singapore; Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia; Centre for Health Research, University of Southern Queensland, Australia
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11
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Harrison AP, Li B, Hsu TH, Chen CJ, Yu WT, Tai J, Lu L, Tai DI. Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes. Diagnostics (Basel) 2023; 13:3225. [PMID: 37892046 PMCID: PMC10605714 DOI: 10.3390/diagnostics13203225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/30/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open
Abstract
INTRODUCTION A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes. MATERIALS AND METHODS Patients (N = 101) who experienced weight changes ≥ 5% were selected for the study, using serial ultrasound studies retrospectively collected from 2013 to 2021. After applying our exclusion criteria, 74 patients from 239 studies were included. We classified images into four scanning views and applied the algorithm. Mean values from 3-5 images in each group were used for the results and correlated against weight changes. RESULTS Images from the left lobe (G1) in 45 patients, right intercostal view (G2) in 67 patients, and subcostal view (G4) in 46 patients were collected. In a head-to-head comparison, G1 versus G2 or G2 versus G4 views showed identical steatosis scores (R2 > 0.86, p < 0.001). The body weight and steatosis scores were significantly correlated (R2 = 0.62, p < 0.001). Significant differences in steatosis scores between the highest and lowest body weight timepoints were found (p < 0.001). Men showed a higher liver steatosis/BMI ratio than women (p = 0.026). CONCLUSIONS The best scanning conditions are 3-5 images from the right intercostal view. The algorithm objectively quantified liver steatosis, which correlated with body weight changes and gender.
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Affiliation(s)
- Adam P. Harrison
- Research Division, Riverain Technologies, Miamisburg, OH 45342, USA;
| | - Bowen Li
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 20818, USA;
| | - Tse-Hwa Hsu
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan; (T.-H.H.); (C.-J.C.); (W.-T.Y.); (J.T.)
| | - Cheng-Jen Chen
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan; (T.-H.H.); (C.-J.C.); (W.-T.Y.); (J.T.)
| | - Wan-Ting Yu
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan; (T.-H.H.); (C.-J.C.); (W.-T.Y.); (J.T.)
| | - Jennifer Tai
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan; (T.-H.H.); (C.-J.C.); (W.-T.Y.); (J.T.)
| | - Le Lu
- DAMO Academy, Alibaba Group, New York, NY 94085, USA;
| | - Dar-In Tai
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan; (T.-H.H.); (C.-J.C.); (W.-T.Y.); (J.T.)
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12
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Vianna P, Calce SI, Boustros P, Larocque-Rigney C, Patry-Beaudoin L, Luo YH, Aslan E, Marinos J, Alamri TM, Vu KN, Murphy-Lavallée J, Billiard JS, Montagnon E, Li H, Kadoury S, Nguyen BN, Gauthier S, Therien B, Rish I, Belilovsky E, Wolf G, Chassé M, Cloutier G, Tang A. Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis. Radiology 2023; 309:e230659. [PMID: 37787678 DOI: 10.1148/radiol.230659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose To evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.
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Affiliation(s)
- Pedro Vianna
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Sara-Ivana Calce
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Pamela Boustros
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Cassandra Larocque-Rigney
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Laurent Patry-Beaudoin
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Yi Hui Luo
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Emre Aslan
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - John Marinos
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Talal M Alamri
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Kim-Nhien Vu
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Jessica Murphy-Lavallée
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Jean-Sébastien Billiard
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Emmanuel Montagnon
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Hongliang Li
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Samuel Kadoury
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Bich N Nguyen
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Shanel Gauthier
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Benjamin Therien
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Irina Rish
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Eugene Belilovsky
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Guy Wolf
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Michaël Chassé
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - Guy Cloutier
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
| | - An Tang
- From the Department of Imaging and Engineering (P.V., S.I.C., C.L.R., L.P.B., E.M., H.L., S.K., M.C., G.C., A.T.), Laboratory of Biorheology and Medical Ultrasonics (P.V., G.C.), and Clinical Laboratory of Image Processing (E.M., A.T.), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering (P.V., G.C.) and Department of Computer Science and Operations Research (S.G., I.R., G.W.), Université de Montréal, Montréal, Canada; Departments of Radiology (S.I.C., P.B., C.L.R., L.P.B., Y.H.L., E.A., J.M., T.M.A., K.N.V., J.M.L., J.S.B., A.T.) and Pathology (B.N.N.), Centre Hospitalier de l'Université de Montréal (CHUM), 1058 rue Saint-Denis, Montréal, QC, Canada H2X 3J4; Department of Computer Engineering, École Polytechnique de Montréal, Montréal, Canada (S.K.); Mila-Quebec Artificial Intelligence Institute, Montréal, Canada (S.G., B.T., I.R., E.B., G.W.); and Department of Computer Science and Software Engineering, Concordia University, Montréal, Canada (B.T., E.B.)
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Yang Y, Liu J, Sun C, Shi Y, Hsing JC, Kamya A, Keller CA, Antil N, Rubin D, Wang H, Ying H, Zhao X, Wu YH, Nguyen M, Lu Y, Yang F, Huang P, Hsing AW, Wu J, Zhu S. Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population. Eur Radiol 2023; 33:5894-5906. [PMID: 36892645 DOI: 10.1007/s00330-023-09515-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 10/21/2022] [Accepted: 02/03/2023] [Indexed: 03/10/2023]
Abstract
OBJECTIVES We aimed to develop and validate a deep learning system (DLS) by using an auxiliary section that extracts and outputs specific ultrasound diagnostic features to improve the explainable, clinical relevant utility of using DLS for detecting NAFLD. METHODS In a community-based study of 4144 participants with abdominal ultrasound scan in Hangzhou, China, we sampled 928 (617 [66.5%] females, mean age: 56 years ± 13 [standard deviation]) participants (2 images per participant) to develop and validate DLS, a two-section neural network (2S-NNet). Radiologists' consensus diagnosis classified hepatic steatosis as none steatosis, mild, moderate, and severe. We also explored the NAFLD detection performance of six one-section neural network models and five fatty liver indices on our data set. We further evaluated the influence of participants' characteristics on the correctness of 2S-NNet by logistic regression. RESULTS Area under the curve (AUROC) of 2S-NNet for hepatic steatosis was 0.90 for ≥ mild, 0.85 for ≥ moderate, and 0.93 for severe steatosis, and was 0.90 for NAFLD presence, 0.84 for moderate to severe NAFLD, and 0.93 for severe NAFLD. The AUROC of NAFLD severity was 0.88 for 2S-NNet, and 0.79-0.86 for one-section models. The AUROC of NAFLD presence was 0.90 for 2S-NNet, and 0.54-0.82 for fatty liver indices. Age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry had no significant impact on the correctness of 2S-NNet (p > 0.05). CONCLUSIONS By using two-section design, 2S-NNet had improved the performance for detecting NAFLD with more explainable, clinical relevant utility than using one-section design. KEY POINTS • Based on the consensus review derived from radiologists, our DLS (2S-NNet) had an AUROC of 0.88 by using two-section design and yielded better performance for detecting NAFLD than using one-section design with more explainable, clinical relevant utility. • The 2S-NNet outperformed five fatty liver indices with the highest AUROCs (0.84-0.93 vs. 0.54-0.82) for different NAFLD severity screening, indicating screening utility of deep learning-based radiology may perform better than blood biomarker panels in epidemiology. • The correctness of 2S-NNet was not significantly influenced by individual's characteristics, including age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry.
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Affiliation(s)
- Yang Yang
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jing Liu
- College of Computer Science and Technology, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, China
| | - Changxuan Sun
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuwei Shi
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Julianna C Hsing
- Center for Policy, Outcomes, and Prevention, Stanford University School of Medicine, Stanford, CA, USA
| | - Aya Kamya
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Cody Auston Keller
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Neha Antil
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Daniel Rubin
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Hongxia Wang
- Department of Ultrasound, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haochao Ying
- Department of Big Data in Health Science, School of Public Health, Zhejiang University, Hangzhou, China
| | - Xueyin Zhao
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi-Hsuan Wu
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, 780 Welch Road, CJ Huang Building, Suite 250D, Stanford, CA, 94305, USA
| | - Mindie Nguyen
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Division of Gastroenterology and Hepatology, Stanford University Medical Center, Stanford, CA, USA
| | - Ying Lu
- Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Fei Yang
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Pinton Huang
- Department of Ultrasound, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ann W Hsing
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, 780 Welch Road, CJ Huang Building, Suite 250D, Stanford, CA, 94305, USA.
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Jian Wu
- Second Affiliated Hospital School of Medicine, School of Public Health, and Institute of Wenzhou, Zhejiang University, Hangzhou, 310058, China.
| | - Shankuan Zhu
- Chronic Disease Research Institute, The Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, No.866 Yu Hang Tang Road, Hangzhou, 310058, Zhejiang, China.
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China.
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Singh S, Hoque S, Zekry A, Sowmya A. Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review. J Med Syst 2023; 47:73. [PMID: 37432493 PMCID: PMC10335966 DOI: 10.1007/s10916-023-01968-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 07/02/2023] [Indexed: 07/12/2023]
Abstract
Medical image analysis plays a pivotal role in the evaluation of diseases, including screening, surveillance, diagnosis, and prognosis. Liver is one of the major organs responsible for key functions of metabolism, protein and hormone synthesis, detoxification, and waste excretion. Patients with advanced liver disease and Hepatocellular Carcinoma (HCC) are often asymptomatic in the early stages; however delays in diagnosis and treatment can lead to increased rates of decompensated liver diseases, late-stage HCC, morbidity and mortality. Ultrasound (US) is commonly used imaging modality for diagnosis of chronic liver diseases that includes fibrosis, cirrhosis and portal hypertension. In this paper, we first provide an overview of various diagnostic methods for stages of liver diseases and discuss the role of Computer-Aided Diagnosis (CAD) systems in diagnosing liver diseases. Second, we review the utility of machine learning and deep learning approaches as diagnostic tools. Finally, we present the limitations of existing studies and outline future directions to further improve diagnostic accuracy, as well as reduce cost and subjectivity, while also improving workflow for the clinicians.
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Affiliation(s)
- Sonit Singh
- School of CSE, UNSW Sydney, High St, Kensington, 2052, NSW, Australia.
| | - Shakira Hoque
- Gastroenterology and Hepatology Department, St George Hospital, Hogben St, Kogarah, 2217, NSW, Australia
| | - Amany Zekry
- St George and Sutherland Clinical Campus, School of Clinical Medicine, UNSW, High St, Kensington, 2052, NSW, Australia
- Gastroenterology and Hepatology Department, St George Hospital, Hogben St, Kogarah, 2217, NSW, Australia
| | - Arcot Sowmya
- School of CSE, UNSW Sydney, High St, Kensington, 2052, NSW, Australia
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15
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Fetzer DT, Pierce TT, Robbin ML, Cloutier G, Mufti A, Hall TJ, Chauhan A, Kubale R, Tang A. US Quantification of Liver Fat: Past, Present, and Future. Radiographics 2023; 43:e220178. [PMID: 37289646 DOI: 10.1148/rg.220178] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Fatty liver disease has a high and increasing prevalence worldwide, is associated with adverse cardiovascular events and higher long-term medical costs, and may lead to liver-related morbidity and mortality. There is an urgent need for accurate, reproducible, accessible, and noninvasive techniques appropriate for detecting and quantifying liver fat in the general population and for monitoring treatment response in at-risk patients. CT may play a potential role in opportunistic screening, and MRI proton-density fat fraction provides high accuracy for liver fat quantification; however, these imaging modalities may not be suited for widespread screening and surveillance, given the high global prevalence. US, a safe and widely available modality, is well positioned as a screening and surveillance tool. Although well-established qualitative signs of liver fat perform well in moderate and severe steatosis, these signs are less reliable for grading mild steatosis and are likely unreliable for detecting subtle changes over time. New and emerging quantitative biomarkers of liver fat, such as those based on standardized measurements of attenuation, backscatter, and speed of sound, hold promise. Evolving techniques such as multiparametric modeling, radiofrequency envelope analysis, and artificial intelligence-based tools are also on the horizon. The authors discuss the societal impact of fatty liver disease, summarize the current state of liver fat quantification with CT and MRI, and describe past, currently available, and potential future US-based techniques for evaluating liver fat. For each US-based technique, they describe the concept, measurement method, advantages, and limitations. © RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.
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Affiliation(s)
- David T Fetzer
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Theodore T Pierce
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Michelle L Robbin
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Guy Cloutier
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Arjmand Mufti
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Timothy J Hall
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Anil Chauhan
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - Reinhard Kubale
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
| | - An Tang
- From the Department of Radiology (D.T.F.) and Department of Internal Medicine, Division of Digestive and Liver Diseases (A.M.), UT Southwestern Medical Center, 5323 Harry Hines Blvd, E6-230-BF, Dallas, TX 75390-9316; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (T.T.P.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R.); Departments of Radiology and Biomedical Engineering, Laboratory of Biorheology and Medical Ultrasonics, University of Montréal Hospital Research Center, Montréal, Quebec, Canada (G.C.); Department of Medical Physics, University of Wisconsin, Madison, Wis (T.J.H.); Department of Radiology, University of Kansas Medical Center, Kansas City, Kan (A.C.); Department of Diagnostic and Interventional Radiology, University Hospital Homburg/Saar, Homburg, Germany (R.K.); and Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM) and Université de Montréal, Montréal, Quebec, Canada (A.T.)
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16
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Gheorghe EC, Nicolau C, Kamal A, Udristoiu A, Gruionu L, Saftoiu A. Artificial Intelligence (AI)-Enhanced Ultrasound Techniques Used in Non-Alcoholic Fatty Liver Disease: Are They Ready for Prime Time? APPLIED SCIENCES 2023; 13:5080. [DOI: 10.3390/app13085080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic liver disease, affecting approximately 2 billion individuals worldwide with a spectrum that can range from simple steatosis to cirrhosis. Typically, the diagnosis of NAFLD is based on imaging studies, but the gold standard remains liver biopsies. Hence, the use of artificial intelligence (AI) in this field, which has recently undergone rapid development in various aspects of medicine, has the potential to accurately diagnose NAFLD and steatohepatitis (NASH). This paper provides an overview of the latest research that employs AI for the diagnosis and staging of NAFLD, as well as applications for future developments in this field.
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Affiliation(s)
- Elena Codruta Gheorghe
- Department of Family Medicine, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania
| | - Carmen Nicolau
- Lotus Image Medical Center, ActaMedica SRL Târgu Mureș, 540084 Târgu Mureș, Romania
| | - Adina Kamal
- Department of Internal Medicine, University of Medicine and Pharmacy Craiova, 200349 Craiova, Romania
| | - Anca Udristoiu
- Faculty of Automation, Computers and Electronics, University of Craiova, 200776 Craiova, Romania
| | - Lucian Gruionu
- Faculty of Mechanics, University of Craiova, 200512 Craiova, Romania
| | - Adrian Saftoiu
- Department of Gastroenterology and Hepatology, University of Medicine and Pharmacy Carol Davila Bucharest, 050474 Bucharest, Romania
- Department of Gastroenterology, Ponderas Academic Hospital, 014142 Bucharest, Romania
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Tahmasebi A, Wang S, Wessner CE, Vu T, Liu JB, Forsberg F, Civan J, Guglielmo FF, Eisenbrey JR. Ultrasound-Based Machine Learning Approach for Detection of Nonalcoholic Fatty Liver Disease. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023. [PMID: 36807314 DOI: 10.1002/jum.16194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/05/2022] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVES Current diagnosis of nonalcoholic fatty liver disease (NAFLD) relies on biopsy or MR-based fat quantification. This prospective study explored the use of ultrasound with artificial intelligence for the detection of NAFLD. METHODS One hundred and twenty subjects with clinical suspicion of NAFLD and 10 healthy volunteers consented to participate in this institutional review board-approved study. Subjects were categorized as NAFLD and non-NAFLD according to MR proton density fat fraction (PDFF) findings. Ultrasound images from 10 different locations in the right and left hepatic lobes were collected following a standard protocol. MRI-based liver fat quantification was used as the reference standard with >6.4% indicative of NAFLD. A supervised machine learning model was developed for assessment of NAFLD. To validate model performance, a balanced testing dataset of 24 subjects was used. Sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy with 95% confidence interval were calculated. RESULTS A total of 1119 images from 106 participants was used for model development. The internal evaluation achieved an average precision of 0.941, recall of 88.2%, and precision of 89.0%. In the testing set AutoML achieved a sensitivity of 72.2% (63.1%-80.1%), specificity of 94.6% (88.7%-98.0%), positive predictive value (PPV) of 93.1% (86.0%-96.7%), negative predictive value of 77.3% (71.6%-82.1%), and accuracy of 83.4% (77.9%-88.0%). The average agreement for an individual subject was 92%. CONCLUSIONS An ultrasound-based machine learning model for identification of NAFLD showed high specificity and PPV in this prospective trial. This approach may in the future be used as an inexpensive and noninvasive screening tool for identifying NAFLD in high-risk patients.
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Affiliation(s)
- Aylin Tahmasebi
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Shuo Wang
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Corinne E Wessner
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Trang Vu
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Ji-Bin Liu
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Flemming Forsberg
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Jesse Civan
- Department of Medicine, Division of Gastroenterology and Hepatology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Flavius F Guglielmo
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - John R Eisenbrey
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
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Uslu F, Bharath AA. TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation. Comput Biol Med 2023; 152:106422. [PMID: 36535210 DOI: 10.1016/j.compbiomed.2022.106422] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 12/02/2022] [Accepted: 12/11/2022] [Indexed: 12/15/2022]
Abstract
Recently, deep networks have shown impressive performance for the segmentation of cardiac Magnetic Resonance Imaging (MRI) images. However, their achievement is proving slow to transition to widespread use in medical clinics because of robustness issues leading to low trust of clinicians to their results. Predicting run-time quality of segmentation masks can be useful to warn clinicians against poor results. Despite its importance, there are few studies on this problem. To address this gap, we propose a quality control method based on the agreement across decoders of a multi-view network, TMS-Net, measured by the cosine similarity. The network takes three view inputs resliced from the same 3D image along different axes. Different from previous multi-view networks, TMS-Net has a single encoder and three decoders, leading to better noise robustness, segmentation performance and run-time quality estimation in our experiments on the segmentation of the left atrium on STACOM 2013 and STACOM 2018 challenge datasets. We also present a way to generate poor segmentation masks by using noisy images generated with engineered noise and Rician noise to simulate undertraining, high anisotropy and poor imaging settings problems. Our run-time quality estimation method show a good classification of poor and good quality segmentation masks with an AUC reaching to 0.97 on STACOM 2018. We believe that TMS-Net and our run-time quality estimation method has a high potential to increase the thrust of clinicians to automatic image analysis tools.
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Affiliation(s)
- Fatmatülzehra Uslu
- Bursa Technical University, Electrical and Electronics Engineering Department, Bursa, 16310, Turkey.
| | - Anil A Bharath
- Imperial College London, Bioengineering Department, London, SW7 2AZ, UK.
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19
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Feature analysis and automatic classification of B-mode ultrasound images of fatty liver. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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20
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Mahzari A. Artificial intelligence in nonalcoholic fatty liver disease. EGYPTIAN LIVER JOURNAL 2022. [DOI: 10.1186/s43066-022-00224-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Abstract
Background
Nonalcoholic fatty liver disease (NAFLD) has led to serious health-related complications worldwide. NAFLD has wide pathological spectra, ranging from simple steatosis to hepatitis to cirrhosis and hepatocellular carcinoma. Artificial intelligence (AI), including machine learning and deep learning algorithms, has provided great advancement and accuracy in identifying, diagnosing, and managing patients with NAFLD and detecting squeal such as advanced fibrosis and risk factors for hepatocellular cancer. This review summarizes different AI algorithms and methods in the field of hepatology, focusing on NAFLD.
Methods
A search of PubMed, WILEY, and MEDLINE databases were taken as relevant publications for this review on the application of AI techniques in detecting NAFLD in suspected population
Results
Out of 495 articles searched in relevant databases, 49 articles were finally included and analyzed. NASH-Scope model accurately distinguished between NAFLD and non-NAFLD and between NAFLD without fibrosis and NASH with fibrosis. The logistic regression (LR) model had the highest accuracy, whereas the support vector machine (SVM) had the highest specificity and precision in diagnosing NAFLD. An extreme gradient boosting model had the highest performance in predicting non-alcoholic steatohepatitis (NASH). Electronic health record (EHR) database studies helped the diagnose NAFLD/NASH. Automated image analysis techniques predicted NAFLD severity. Deep learning radiomic elastography (DLRE) had perfect accuracy in diagnosing the cases of advanced fibrosis.
Conclusion
AI in NAFLD has streamlined specific patient identification and has eased assessment and management methods of patients with NAFLD.
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Alshagathrh FM, Househ MS. Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120748. [PMID: 36550954 PMCID: PMC9774180 DOI: 10.3390/bioengineering9120748] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/20/2022] [Accepted: 10/30/2022] [Indexed: 12/05/2022]
Abstract
BACKGROUND Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. OBJECTIVE This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. METHODOLOGY A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. RESULTS Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). CONCLUSION AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary.
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22
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Ma L, Wang R, He Q, Huang L, Wei X, Lu X, Du Y, Luo J, Liao H. Artificial intelligence-based ultrasound imaging technologies for hepatic diseases. ILIVER 2022; 1:252-264. [DOI: 10.1016/j.iliver.2022.11.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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23
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Ülger Y, Delik A. Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks. NUCLEOSIDES, NUCLEOTIDES & NUCLEIC ACIDS 2022; 42:398-406. [PMID: 36448439 DOI: 10.1080/15257770.2022.2152046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple steatosis, steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). Genetic, nutritional factors, obesity, insulin resistance, gut microbiota are among the risk factors for NAFLD. The genetic variant Patatin-like phospholipase domain-containing protein 3 (PNPLA3) plays an important role in the development of a number of liver diseases ranging from steatosis, chronic hepatitis, cirrhosis and HCC. Due to the increase in the prevalence of NAFLD, new models are being developed with machine learning, deep learning, artificial neural network (ANN) algorithms in the field of artificial intelligence (AI) to determine low-cost, noninvasive diagnostic methods. Models developed with ANN from AI modules are important in order to examine biochemical and genomic information in detail in the diagnosis of NAFLD. The aim of this study is to develop a simple ANN model using biochemical and genotypic parameters in the diagnosis of NAFLD. A total of 300 patients followed up with the diagnosis of NAFLD and 100 controls were included in the study. The data set was divided into two as training and test set. Genotyping of PNPLA3 (CC, CG, GG) as genomic analysis was performed with real time PCR device. The algorithm used for the diagnosis of NAFLD was designed using age, body mass index (BMI), mean platelet volume (MPV), insulin resistance (IR), alanine aminotransferase (ALT), genotype PNPLA3 (CC, CG, GG) parameters. MLP Classifier algorithm from ANN was used in the development of the model. ANN algorithms are used in python programming language. Statistical analyzes were made in SPSS program. Percent accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall, and f1-score results were determined. The accuracy percentage was determined as 0.979 in the train set and 0.970 in the test set. The Log Loss value was set to 0.09. The developed neural network achieved an accuracy percentage of 97.0% during testing, with an area under the ROC curve value of 0.95. We think that the ANN model developed with genomic and biochemical parameters can be used as a cost-effective, noninvasive new predictive diagnostic model in clinical practice in the diagnosis of NAFLD.
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Affiliation(s)
- Yakup Ülger
- Faculty of Medicine, Department of Gastroenterology, Cukurova University, Adana, Turkey
| | - Anıl Delik
- Faculty of Medicine, Department of Gastroenterology, Cukurova University, Adana, Turkey
- Faculty of Science and Literature Department of Biology, Cukurova University, Adana, Turkey
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Li B, Tai DI, Yan K, Chen YC, Chen CJ, Huang SF, Hsu TH, Yu WT, Xiao J, Le L, Harrison AP. Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning. World J Gastroenterol 2022; 28:2494-2508. [PMID: 35979264 PMCID: PMC9258285 DOI: 10.3748/wjg.v28.i22.2494] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 02/03/2022] [Accepted: 04/25/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Hepatic steatosis is a major cause of chronic liver disease. Two-dimensional (2D) ultrasound is the most widely used non-invasive tool for screening and monitoring, but associated diagnoses are highly subjective.
AIM To develop a scalable deep learning (DL) algorithm for quantitative scoring of liver steatosis from 2D ultrasound images.
METHODS Using multi-view ultrasound data from 3310 patients, 19513 studies, and 228075 images from a retrospective cohort of patients received elastography, we trained a DL algorithm to diagnose steatosis stages (healthy, mild, moderate, or severe) from clinical ultrasound diagnoses. Performance was validated on two multi-scanner unblinded and blinded (initially to DL developer) histology-proven cohorts (147 and 112 patients) with histopathology fatty cell percentage diagnoses and a subset with FibroScan diagnoses. We also quantified reliability across scanners and viewpoints. Results were evaluated using Bland-Altman and receiver operating characteristic (ROC) analysis.
RESULTS The DL algorithm demonstrated repeatable measurements with a moderate number of images (three for each viewpoint) and high agreement across three premium ultrasound scanners. High diagnostic performance was observed across all viewpoints: Areas under the curve of the ROC to classify mild, moderate, and severe steatosis grades were 0.85, 0.91, and 0.93, respectively. The DL algorithm outperformed or performed at least comparably to FibroScan control attenuation parameter (CAP) with statistically significant improvements for all levels on the unblinded histology-proven cohort and for “= severe” steatosis on the blinded histology-proven cohort.
CONCLUSION The DL algorithm provides a reliable quantitative steatosis assessment across view and scanners on two multi-scanner cohorts. Diagnostic performance was high with comparable or better performance than the CAP.
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Affiliation(s)
- Bowen Li
- Research and Development, PAII Inc., Bethesda, MD 20817, United States
| | - Dar-In Tai
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan
| | - Ke Yan
- Research and Development, PAII Inc., Bethesda, MD 20817, United States
| | - Yi-Cheng Chen
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan
| | - Cheng-Jen Chen
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan
| | - Shiu-Feng Huang
- Division of Molecular and Genomic Medicine, National Health Research Institute, Taoyuan 33305, Taiwan
| | - Tse-Hwa Hsu
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan
| | - Wan-Ting Yu
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan 33305, Taiwan
| | - Jing Xiao
- Research and Development, Ping An Insurance Group, Shenzhen 518001, Guangdong, China
| | - Lu Le
- Research and Development, PAII Inc., Bethesda, MD 20817, United States
| | - Adam P Harrison
- Research and Development, PAII Inc., Bethesda, MD 20817, United States
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25
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Li Y, Wang X, Zhang J, Zhang S, Jiao J. Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review. Rev Endocr Metab Disord 2022; 23:387-400. [PMID: 34396467 DOI: 10.1007/s11154-021-09681-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/07/2021] [Indexed: 10/20/2022]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is one of the most important causes of chronic liver disease in the world, it has been found that cardiovascular and renal risks and diseases are also highly prevalent in adults with NAFLD. Diagnosis and treatment of NAFLD face many challenges, although the medical science has been very developed. Efficiency, accuracy and individualization are the main goals to be solved. Evaluation of the severity of NAFLD involves a variety of clinical parameters, how to optimize non-invasive evaluation methods is a necessary issue that needs to be discussed in this field. Artificial intelligence (AI) has become increasingly widespread in healthcare applications, and it has been also brought many new insights into better analyzing chronic liver disease, including NAFLD. This paper reviewed AI related researches in NAFLD field published recently, summarized diagnostic models based on electronic health record and lab test, ultrasound and radio imaging, and liver histopathological data, described the application of therapeutic models in personalized lifestyle guidance and the development of drugs for NAFLD. In addition, we also analyzed present AI models in distinguishing healthy VS NAFLD/NASH, and fibrosis VS non-fibrosis in the evaluation of NAFLD progression. We hope to provide alternative directions for the future research.
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Affiliation(s)
- Yifang Li
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Xuetao Wang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Jun Zhang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Shanshan Zhang
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China
| | - Jian Jiao
- Department of Gastroenterolgy & Hepatology, China-Japan Union Hospital, Jilin University, Changchun, 130033, China.
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Zhou B, Yang X, Curran WJ, Liu T. Artificial Intelligence in Quantitative Ultrasound Imaging: A Survey. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022; 41:1329-1342. [PMID: 34467542 DOI: 10.1002/jum.15819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 08/01/2021] [Accepted: 08/16/2021] [Indexed: 06/13/2023]
Abstract
Quantitative ultrasound (QUS) imaging is a safe, reliable, inexpensive, and real-time technique to extract physically descriptive parameters for assessing pathologies. Compared with other major imaging modalities such as computed tomography and magnetic resonance imaging, QUS suffers from several major drawbacks: poor image quality and inter- and intra-observer variability. Therefore, there is a great need to develop automated methods to improve the image quality of QUS. In recent years, there has been increasing interest in artificial intelligence (AI) applications in medical imaging, and a large number of research studies in AI in QUS have been conducted. The purpose of this review is to describe and categorize recent research into AI applications in QUS. We first introduce the AI workflow and then discuss the various AI applications in QUS. Finally, challenges and future potential AI applications in QUS are discussed.
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Affiliation(s)
- Boran Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, USA
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Zhou H, Zhou Y, Ding J, Chen Y, Wen J, Zhao L, Zhang Q, Jing X. Clinical evaluation of grayscale and linear scale hepatorenal indices for fatty liver quantification: a prospective study of a native Chinese population. Abdom Radiol (NY) 2022; 47:1321-1332. [PMID: 35150314 DOI: 10.1007/s00261-022-03434-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND AND AIMS Hepato-renal index (HRI) has been investigated extensively in various clinical studies. New linear scale HRI (LS-HRI) is proposed as an alternative to conventional grayscale HRI (GS-HRI) that suffers from lack of a widely accepted cut-off value for differentiation of fatty from normal livers. To investigate the diagnostic performance of conventional GS-HRI and new LS-HRI for a relatively large Chinese population with NAFLD using a well-established ultrasonographic fatty liver indicator (US-FLI) as the reference standard for steatosis grades. MATERIALS AND METHODS A total of 106 patients with various stages of NAFLD were prospectively enrolled. All ultrasound images for these patients were first acquired by a highly experienced ultrasound doctor and their US-FLI scores then obtained by the same doctor. Both GS-HRI and LS-HRI values were measured off-line by two additional ultrasound doctors. Four steatosis grades were determined from US-FLI scores for steatosis detection and staging. RESULTS Inter-observer agreements for both GS-HRI and LS-HRI were excellent with the respective concordance correlation coefficient (CCC) of 0.900 for GS-HRI and 0.822 for LS-HRI. A linear correlation to US-FLI for LS-HRI (R = 0.74) was substantially superior to that for GS-HRI (R = 0.46). LS-HRI had a sensitivity of 85.9% and a specificity of 96.3% to differentiate steatosis from the normal liver (AUROC: 95.5%) while GS-HRI had a sensitivity of 85.9% and a specificity of 92.6% to distinguish steatosis from the normal liver (AUROC: 94.7%). CONCLUSIONS Both GS-HRI and LS-HRI measurements are reproducible between two ultrasonographic clinicians and are evidently effective for steatosis detection.
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Affiliation(s)
- Hongyu Zhou
- Department of Ultrasound, The Third Central Hospital of Tianjin/Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases/Artificial Cell Engineering Technology Research Center, Tianjin, China/Tianjin Institute of Hepatobiliary Disease, Tianjin, China, 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Yan Zhou
- Department of Ultrasound, The Third Central Hospital of Tianjin/Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases/Artificial Cell Engineering Technology Research Center, Tianjin, China/Tianjin Institute of Hepatobiliary Disease, Tianjin, China, 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Jianmin Ding
- Department of Ultrasound, The Third Central Hospital of Tianjin/Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases/Artificial Cell Engineering Technology Research Center, Tianjin, China/Tianjin Institute of Hepatobiliary Disease, Tianjin, China, 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Ying Chen
- Department of Ultrasound, The Third Central Hospital of Tianjin/Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases/Artificial Cell Engineering Technology Research Center, Tianjin, China/Tianjin Institute of Hepatobiliary Disease, Tianjin, China, 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Jing Wen
- Department of Ultrasound, The Third Central Hospital of Tianjin/Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases/Artificial Cell Engineering Technology Research Center, Tianjin, China/Tianjin Institute of Hepatobiliary Disease, Tianjin, China, 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Lei Zhao
- Department of Ultrasound, The Third Central Hospital of Tianjin/Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases/Artificial Cell Engineering Technology Research Center, Tianjin, China/Tianjin Institute of Hepatobiliary Disease, Tianjin, China, 83 Jintang Road, Hedong District, Tianjin, 300170, China
| | - Qian Zhang
- The Third Central Clinical College of Tianjin Medical University, Tianjin, 300170, China
| | - Xiang Jing
- Department of Ultrasound, The Third Central Hospital of Tianjin/Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases/Artificial Cell Engineering Technology Research Center, Tianjin, China/Tianjin Institute of Hepatobiliary Disease, Tianjin, China, 83 Jintang Road, Hedong District, Tianjin, 300170, China.
- Department of Ultrasound, The Third Central Hospital of Tianjin, Tianjin, China, 83 Jintang Road, Hedong District, Tianjin, 300170, China.
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Wu L, Dong B, Liu X, Hong W, Chen L, Gao K, Sheng Q, Yu Y, Zhao L, Zhang Y. Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation. Front Pediatr 2022; 9:770182. [PMID: 35118028 PMCID: PMC8805220 DOI: 10.3389/fped.2021.770182] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 12/20/2021] [Indexed: 01/10/2023] Open
Abstract
Standard echocardiographic view recognition is a prerequisite for automatic diagnosis of congenital heart defects (CHDs). This study aims to evaluate the feasibility and accuracy of standard echocardiographic view recognition in the diagnosis of CHDs in children using convolutional neural networks (CNNs). A new deep learning-based neural network method was proposed to automatically and efficiently identify commonly used standard echocardiographic views. A total of 367,571 echocardiographic image slices from 3,772 subjects were used to train and validate the proposed echocardiographic view recognition model where 23 standard echocardiographic views commonly used to diagnose CHDs in children were identified. The F1 scores of a majority of views were all ≥0.90, including subcostal sagittal/coronal view of the atrium septum, apical four-chamber view, apical five-chamber view, low parasternal four-chamber view, sax-mid, sax-basal, parasternal long-axis view of the left ventricle (PSLV), suprasternal long-axis view of the entire aortic arch, M-mode echocardiographic recording of the aortic (M-AO) and the left ventricle at the level of the papillary muscle (M-LV), Doppler recording from the mitral valve (DP-MV), the tricuspid valve (DP-TV), the ascending aorta (DP-AAO), the pulmonary valve (DP-PV), and the descending aorta (DP-DAO). This study provides a solid foundation for the subsequent use of artificial intelligence (AI) to identify CHDs in children.
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Affiliation(s)
- Lanping Wu
- Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bin Dong
- Shanghai Engineering Research Center of Intelligence Pediatrics, Shanghai, China
| | - Xiaoqing Liu
- Deepwise Artificial Intelligence Laboratory, Beijing, China
| | - Wenjing Hong
- Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lijun Chen
- Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Kunlun Gao
- Deepwise Artificial Intelligence Laboratory, Beijing, China
| | - Qiuyang Sheng
- Deepwise Artificial Intelligence Laboratory, Beijing, China
| | - Yizhou Yu
- Deepwise Artificial Intelligence Laboratory, Beijing, China
| | - Liebin Zhao
- Shanghai Engineering Research Center of Intelligence Pediatrics, Shanghai, China
| | - Yuqi Zhang
- Department of Pediatric Cardiology, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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Atsawarungruangkit A, Laoveeravat P, Promrat K. Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database. World J Hepatol 2021; 13:1417-1427. [PMID: 34786176 PMCID: PMC8568572 DOI: 10.4254/wjh.v13.i10.1417] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 05/11/2021] [Accepted: 09/19/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, affecting over 30% of the United States population. Early patient identification using a simple method is highly desirable.
AIM To create machine learning models for predicting NAFLD in the general United States population.
METHODS Using the NHANES 1988-1994. Thirty NAFLD-related factors were included. The dataset was divided into the training (70%) and testing (30%) datasets. Twenty-four machine learning algorithms were applied to the training dataset. The best-performing models and another interpretable model (i.e., coarse trees) were tested using the testing dataset.
RESULTS There were 3235 participants (n = 3235) that met the inclusion criteria. In the training phase, the ensemble of random undersampling (RUS) boosted trees had the highest F1 (0.53). In the testing phase, we compared selective machine learning models and NAFLD indices. Based on F1, the ensemble of RUS boosted trees remained the top performer (accuracy 71.1% and F1 0.56) followed by the fatty liver index (accuracy 68.8% and F1 0.52). A simple model (coarse trees) had an accuracy of 74.9% and an F1 of 0.33.
CONCLUSION Not every machine learning model is complex. Using a simpler model such as coarse trees, we can create an interpretable model for predicting NAFLD with only two predictors: fasting C-peptide and waist circumference. Although the simpler model does not have the best performance, its simplicity is useful in clinical practice.
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Affiliation(s)
- Amporn Atsawarungruangkit
- Division of Gastroenterology, Warren Alpert Medical School, Brown University, Providence, RI 02903, United States
| | - Passisd Laoveeravat
- Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Kittichai Promrat
- Division of Gastroenterology, Warren Alpert Medical School, Brown University, Providence, RI 02903, United States
- Division of Gastroenterology and Hepatology, Providence VA Medical Center, Providence, RI 02908, United States
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Chou TH, Yeh HJ, Chang CC, Tang JH, Kao WY, Su IC, Li CH, Chang WH, Huang CK, Sufriyana H, Su ECY. Deep learning for abdominal ultrasound: A computer-aided diagnostic system for the severity of fatty liver. J Chin Med Assoc 2021; 84:842-850. [PMID: 34282076 DOI: 10.1097/jcma.0000000000000585] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The prevalence of nonalcoholic fatty liver disease is increasing over time worldwide, with similar trends to those of diabetes and obesity. A liver biopsy, the gold standard of diagnosis, is not favored due to its invasiveness. Meanwhile, noninvasive evaluation methods of fatty liver are still either very expensive or demonstrate poor diagnostic performances, thus, limiting their applications. We developed neural network-based models to assess fatty liver and classify the severity using B-mode ultrasound (US) images. METHODS We followed standards for reporting of diagnostic accuracy guidelines to report this study. In this retrospective study, we utilized B-mode US images from a consecutive series of patients to develop four-class, two-class, and three-class diagnostic prediction models. The images were eligible if confirmed by at least two gastroenterologists. We compared pretrained convolutional neural network models, consisting of visual geometry group (VGG)19, ResNet-50 v2, MobileNet v2, Xception, and Inception v2. For validation, we utilized 20% of the dataset resulting in >100 images for each severity category. RESULTS There were 21,855 images from 2,070 patients classified as normal (N = 11,307), mild (N = 4,467), moderate (N = 3,155), or severe steatosis (N = 2,926). We used ResNet-50 v2 for the final model as the best ones. The areas under the receiver operating characteristic curves were 0.974 (mild steatosis vs others), 0.971 (moderate steatosis vs others), 0.981 (severe steatosis vs others), 0.985 (any severity vs normal), and 0.996 (moderate-to-severe steatosis/clinically abnormal vs normal-to-mild steatosis/clinically normal). CONCLUSION Our deep learning models achieved comparable predictive performances to the most accurate, yet expensive, noninvasive diagnostic methods for fatty liver. Because of the discriminative ability, including for mild steatosis, significant impacts on clinical applications for fatty liver are expected. However, we need to overcome machine-dependent variation, motion artifacts, lacking of second confirmation from any other tools, and hospital-dependent regional bias.
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Affiliation(s)
- Tsung-Hsien Chou
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan, ROC
| | - Hsing-Jung Yeh
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan, ROC
| | - Chun-Chao Chang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan, ROC
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, ROC
| | - Jui-Hsiang Tang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan, ROC
| | - Wei-Yu Kao
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan, ROC
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan, ROC
| | - I-Chia Su
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan, ROC
| | - Chien-Hung Li
- Acer Value Lab Advanced Tech Business Unit, Acer Incorporated, New Taipei City, Taiwan, ROC
| | - Wei-Hao Chang
- Acer Value Lab Advanced Tech Business Unit, Acer Incorporated, New Taipei City, Taiwan, ROC
| | - Chun-Kai Huang
- Acer Value Lab Advanced Tech Business Unit, Acer Incorporated, New Taipei City, Taiwan, ROC
| | - Herdiantri Sufriyana
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan, ROC
- Department of Medical Physiology, College of Medicine, University of Nahdlatul Ulama Surabaya, Surabaya, Indonesia
| | - Emily Chia-Yu Su
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan, ROC
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan, ROC
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31
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Rhyou SY, Yoo JC. Cascaded Deep Learning Neural Network for Automated Liver Steatosis Diagnosis Using Ultrasound Images. SENSORS 2021; 21:s21165304. [PMID: 34450746 PMCID: PMC8398227 DOI: 10.3390/s21165304] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/27/2021] [Accepted: 08/03/2021] [Indexed: 12/13/2022]
Abstract
Diagnosing liver steatosis is an essential precaution for detecting hepatocirrhosis and liver cancer in the early stages. However, automatic diagnosis of liver steatosis from ultrasound (US) images remains challenging due to poor visual quality from various origins, such as speckle noise and blurring. In this paper, we propose a fully automated liver steatosis prediction model using three deep learning neural networks. As a result, liver steatosis can be automatically detected with high accuracy and precision. First, transfer learning is used for semantically segmenting the liver and kidney (L-K) on parasagittal US images, and then cropping the L-K area from the original US images. The second neural network also involves semantic segmentation by checking the presence of a ring that is typically located around the kidney and cropping of the L-K area from the original US images. These cropped L-K areas are inputted to the final neural network, SteatosisNet, in order to grade the severity of fatty liver disease. The experimental results demonstrate that the proposed model can predict fatty liver disease with the sensitivity of 99.78%, specificity of 100%, PPV of 100%, NPV of 99.83%, and diagnostic accuracy of 99.91%, which is comparable to the common results annotated by medical experts.
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Affiliation(s)
| | - Jae-Chern Yoo
- Correspondence: ; Tel.: +82-31-299-4591; Fax: +82-31-290-7948
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Sabottke CF, Spieler BM, Moawad AW, Elsayes KM. Artificial Intelligence in Imaging of Chronic Liver Diseases: Current Update and Future Perspectives. Magn Reson Imaging Clin N Am 2021; 29:451-463. [PMID: 34243929 DOI: 10.1016/j.mric.2021.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Here we review artificial intelligence (AI) models which aim to assess various aspects of chronic liver disease. Despite the clinical importance of hepatocellular carcinoma in the setting of chronic liver disease, we focus this review on AI models which are not lesion-specific and instead review models developed for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. Optimization of these models offers the opportunity to potentially reduce the need for invasive procedures such as catheterization to measure hepatic venous pressure gradient or biopsy to assess fibrosis and steatosis. We compare the performance of these AI models amongst themselves as well as to radiomics approaches and alternate modality assessments. We conclude that these models show promising performance and merit larger-scale evaluation. We review artificial intelligence models that aim to assess various aspects of chronic liver disease aside from hepatocellular carcinoma. We focus this review on models for liver parenchyma segmentation, evaluation of portal circulation, assessment of hepatic fibrosis, and identification of hepatic steatosis. We conclude that these models show promising performance and merit a larger scale evaluation.
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Affiliation(s)
- Carl F Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, 1501 N. Campbell, P.O. Box 245067, Tucson, AZ 85724-5067, USA.
| | - Bradley M Spieler
- Department of Radiology, Louisiana State University Health Sciences Center, 1542 Tulane Avenue, Rm 343, New Orleans, LA 70112, USA
| | - Ahmed W Moawad
- Department of Imaging Physics, The University of Texas, MD Anderson Cancer Center, Unit 1472, P.O. Box 301402, Houston, TX 77230-1402, USA
| | - Khaled M Elsayes
- Department of Abdominal Imaging, The University of Texas, MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030, USA
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Xiang K, Jiang B, Shang D. The overview of the deep learning integrated into the medical imaging of liver: a review. Hepatol Int 2021; 15:868-880. [PMID: 34264509 DOI: 10.1007/s12072-021-10229-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/24/2021] [Indexed: 12/13/2022]
Abstract
Deep learning (DL) is a recently developed artificial intelligent method that can be integrated into numerous fields. For the imaging diagnosis of liver disease, several remarkable outcomes have been achieved with the application of DL currently. This advanced algorithm takes part in various sections of imaging processing such as liver segmentation, lesion delineation, disease classification, process optimization, etc. The DL optimized imaging diagnosis shows a broad prospect instead of the pathological biopsy for the advantages of convenience, safety, and inexpensiveness. In this paper, we reviewed the published representative DL-related hepatic imaging works, described the general situation of this new-rising technology in medical liver imaging and explored the future direction of DL development.
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Affiliation(s)
- Kailai Xiang
- Department of General Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China.,Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China
| | - Baihui Jiang
- Department of Ophthalmology, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China
| | - Dong Shang
- Department of General Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China. .,Clinical Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China.
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Popa SL, Ismaiel A, Cristina P, Cristina M, Chiarioni G, David L, Dumitrascu DL. Non-Alcoholic Fatty Liver Disease: Implementing Complete Automated Diagnosis and Staging. A Systematic Review. Diagnostics (Basel) 2021; 11:diagnostics11061078. [PMID: 34204822 PMCID: PMC8231502 DOI: 10.3390/diagnostics11061078] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/05/2021] [Accepted: 06/10/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) is a fast-growing pathology around the world, being considered the most common chronic liver disease. It is diagnosed based on the presence of steatosis in more than 5% of hepatocytes without significant alcohol consumption. This review aims to provide a comprehensive overview of current studies of artificial intelligence (AI) applications that may help physicians in implementing a complete automated NAFLD diagnosis and staging. Methods: PubMed, EMBASE, Cochrane Library, and WILEY databases were screened for relevant publications in relation to AI applications in NAFLD. The search terms included: (non-alcoholic fatty liver disease OR NAFLD) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR computer-aided diagnosis OR digital pathology OR automated ultrasound OR automated computer tomography OR automated magnetic imaging OR electronic health records). Results: Our search identified 37 articles about automated NAFLD diagnosis, out of which 15 articles analyzed imagistic techniques, 15 articles analyzed digital pathology, and 7 articles analyzed electronic health records (EHC). All studies included in this review show an accurate capacity of automated diagnosis and staging in NAFLD using AI-based software. Conclusions: We found significant evidence demonstrating that implementing a complete automated system for NAFLD diagnosis, staging, and risk stratification is currently possible, considering the accuracy, sensibility, and specificity of available AI-based tools.
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Affiliation(s)
- Stefan L. Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
- Correspondence:
| | - Pop Cristina
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania; (P.C.); (M.C.)
| | - Mogosan Cristina
- Department of Pharmacology, Physiology and Pathophysiology, Faculty of Pharmacy, “Iuliu Hațieganu” University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania; (P.C.); (M.C.)
| | - Giuseppe Chiarioni
- Division of Gastroenterology, University of Verona, 1-37126 AOUI Verona, Italy;
| | - Liliana David
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
| | - Dan L. Dumitrascu
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania; (S.L.P.); (L.D.); (D.L.D.)
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Saffari SZ, Tabatabaey-Mashadi N, Sadeghi Bajestani G, Razmpour F, Alamdaran SA. Challenging the published fatty liver disease integrated index based on ultrasound images. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Cardobi N, Dal Palù A, Pedrini F, Beleù A, Nocini R, De Robertis R, Ruzzenente A, Salvia R, Montemezzi S, D’Onofrio M. An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging. Cancers (Basel) 2021; 13:2162. [PMID: 33946223 PMCID: PMC8124771 DOI: 10.3390/cancers13092162] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) is one of the most promising fields of research in medical imaging so far. By means of specific algorithms, it can be used to help radiologists in their routine workflow. There are several papers that describe AI approaches to solve different problems in liver and pancreatic imaging. These problems may be summarized in four different categories: segmentation, quantification, characterization and image quality improvement. Segmentation is usually the first step of successive elaborations. If done manually, it is a time-consuming process. Therefore, the semi-automatic and automatic creation of a liver or a pancreatic mask may save time for other evaluations, such as quantification of various parameters, from organs volume to their textural features. The alterations of normal liver and pancreas structure may give a clue to the presence of a diffuse or focal pathology. AI can be trained to recognize these alterations and propose a diagnosis, which may then be confirmed or not by radiologists. Finally, AI may be applied in medical image reconstruction in order to increase image quality, decrease dose administration (referring to computed tomography) and reduce scan times. In this article, we report the state of the art of AI applications in these four main categories.
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Affiliation(s)
- Nicolò Cardobi
- Radiology Unit, Department of Pathology and Diagnostics, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy; (R.D.R.); (S.M.)
| | - Alessandro Dal Palù
- Department of Mathematical, Physical and Computer Sciences, University of Parma, 43121 Parma, Italy;
| | - Federica Pedrini
- Department of Radiology, G.B. Rossi University Hospital, University of Verona, 37129 Verona, Italy; (F.P.); (A.B.); (M.D.)
| | - Alessandro Beleù
- Department of Radiology, G.B. Rossi University Hospital, University of Verona, 37129 Verona, Italy; (F.P.); (A.B.); (M.D.)
| | - Riccardo Nocini
- Otolaryngology-Head and Neck Surgery Department, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy;
| | - Riccardo De Robertis
- Radiology Unit, Department of Pathology and Diagnostics, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy; (R.D.R.); (S.M.)
| | - Andrea Ruzzenente
- Department of Surgery, General and Hepatobiliary Surgery, University Hospital G.B. Rossi, University and Hospital Trust of Verona, 37126 Verona, Italy;
| | - Roberto Salvia
- Unit of General and Pancreatic Surgery, Department of Surgery and Oncology, University of Verona Hospital Trust, 37126 Verona, Italy;
| | - Stefania Montemezzi
- Radiology Unit, Department of Pathology and Diagnostics, University Hospital of Verona, Piazzale Aristide Stefani, 1, 37126 Verona, Italy; (R.D.R.); (S.M.)
| | - Mirko D’Onofrio
- Department of Radiology, G.B. Rossi University Hospital, University of Verona, 37129 Verona, Italy; (F.P.); (A.B.); (M.D.)
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Cao JS, Lu ZY, Chen MY, Zhang B, Juengpanich S, Hu JH, Li SJ, Topatana W, Zhou XY, Feng X, Shen JL, Liu Y, Cai XJ. Artificial intelligence in gastroenterology and hepatology: Status and challenges. World J Gastroenterol 2021; 27:1664-1690. [PMID: 33967550 PMCID: PMC8072192 DOI: 10.3748/wjg.v27.i16.1664] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/11/2021] [Accepted: 03/17/2021] [Indexed: 02/06/2023] Open
Abstract
Originally proposed by John McCarthy in 1955, artificial intelligence (AI) has achieved a breakthrough and revolutionized the processing methods of clinical medicine with the increasing workloads of medical records and digital images. Doctors are paying attention to AI technologies for various diseases in the fields of gastroenterology and hepatology. This review will illustrate AI technology procedures for medical image analysis, including data processing, model establishment, and model validation. Furthermore, we will summarize AI applications in endoscopy, radiology, and pathology, such as detecting and evaluating lesions, facilitating treatment, and predicting treatment response and prognosis with excellent model performance. The current challenges for AI in clinical application include potential inherent bias in retrospective studies that requires larger samples for validation, ethics and legal concerns, and the incomprehensibility of the output results. Therefore, doctors and researchers should cooperate to address the current challenges and carry out further investigations to develop more accurate AI tools for improved clinical applications.
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Affiliation(s)
- Jia-Sheng Cao
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Zi-Yi Lu
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Ming-Yu Chen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Bin Zhang
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Sarun Juengpanich
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Jia-Hao Hu
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Shi-Jie Li
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Win Topatana
- Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xue-Yin Zhou
- School of Medicine, Wenzhou Medical University, Wenzhou 325035, Zhejiang Province, China
| | - Xu Feng
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Ji-Liang Shen
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
| | - Yu Liu
- College of Life Sciences, Zhejiang University, Hangzhou 310058, Zhejiang Province, China
| | - Xiu-Jun Cai
- Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou 310016, Zhejiang Province, China
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Dercle L, Lu L, Schwartz LH, Qian M, Tejpar S, Eggleton P, Zhao B, Piessevaux H. Radiomics Response Signature for Identification of Metastatic Colorectal Cancer Sensitive to Therapies Targeting EGFR Pathway. J Natl Cancer Inst 2021; 112:902-912. [PMID: 32016387 DOI: 10.1093/jnci/djaa017] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Revised: 11/05/2019] [Accepted: 01/24/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The authors sought to forecast survival and enhance treatment decisions for patients with liver metastatic colorectal cancer by using on-treatment radiomics signature to predict tumor sensitiveness to irinotecan, 5-fluorouracil, and leucovorin (FOLFIRI) alone (F) or in combination with cetuximab (FC). METHODS We retrospectively analyzed 667 metastatic colorectal cancer patients treated with F or FC. Computed tomography quality was classified as high (HQ) or standard (SD). Four datasets were created using the nomenclature (treatment) - (quality). Patients were randomly assigned (2:1) to training or validation sets: FCHQ: 78:38, FCSD: 124:62, FHQ: 78:51, FSD: 158:78. Four tumor-imaging biomarkers measured quantitative radiomics changes between standard of care computed tomography scans at baseline and 8 weeks. Using machine learning, the performance of the signature to classify tumors as treatment sensitive or treatment insensitive was trained and validated using receiver operating characteristic (ROC) curves. Hazard ratio and Cox regression models evaluated association with overall survival (OS). RESULTS The signature (area under the ROC curve [95% confidence interval (CI)]) used temporal decrease in tumor spatial heterogeneity plus boundary infiltration to successfully predict sensitivity to antiepidermal growth factor receptor therapy (FCHQ: 0.80 [95% CI = 0.69 to 0.94], FCSD: 0.72 [95% CI = 0.59 to 0.83]) but failed with chemotherapy (FHQ: 0.59 [95% CI = 0.44 to 0.72], FSD: 0.55 [95% CI = 0.43 to 0.66]). In cetuximab-containing sets, radiomics signature outperformed existing biomarkers (KRAS-mutational status, and tumor shrinkage by RECIST 1.1) for detection of treatment sensitivity and was strongly associated with OS (two-sided P < .005). CONCLUSIONS Radiomics response signature can serve as an intermediate surrogate marker of OS. The signature outperformed known biomarkers in providing an early prediction of treatment sensitivity and could be used to guide cetuximab treatment continuation decisions.
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Affiliation(s)
- Laurent Dercle
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY, USA.,Gustave Roussy, Université Paris-Saclay, Villejuif, France
| | - Lin Lu
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY, USA
| | - Lawrence H Schwartz
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY, USA
| | - Min Qian
- Department of Biostatistics, Columbia University Medical Center, New York, NY, USA
| | - Sabine Tejpar
- Molecular Digestive Oncology, University Hospitals Leuven and KU Leuven, Leuven, Belgium
| | | | - Binsheng Zhao
- Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, NY, USA
| | - Hubert Piessevaux
- Department of Hepato-Gastroenterology, Cliniques Universitaires Saint-Luc, UCLouvain Brussels, Brussels, Belgium
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Liu B, Xu Z, Wang Q, Niu X, Chan WX, Hadi W, Yap CH. A denoising and enhancing method framework for 4D ultrasound images of human fetal heart. Quant Imaging Med Surg 2021; 11:1567-1585. [PMID: 33816192 DOI: 10.21037/qims-20-818] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background 4D ultrasound images of human fetal heart are important for medical applications such as evaluation of fetal heart function and early diagnosis of congenital heart diseases. However, due to the high noise and low contrast characteristics in fetal ultrasound images, denoising and enhancements are important. Methods In this paper, a special method framework for denoising and enhancing is proposed. It consists of a 4D-NLM (non-local means) denoising method for 4D fetal heart ultrasound image sequence, which takes advantage of context similar information in neighboring images to denoise the target image, and an enhancing method called the Adaptive Clipping for Each Histogram Pillar (ACEHP), which is designed to enhance myocardial spaces to distinguish them from blood spaces. Results Denoising and enhancing experiments show that 4D-NLM method has better denoising effect than several classical and state-of-the-art methods such as NLM and WNNM. Similarly, ACEHP method can keep noise level low while enhancing myocardial regions better than several classical and state-of-the-art methods such as CLAHE and SVDDWT. Furthermore, in the volume rendering after the combined "4D-NLM+ACEHP" processing, the cardiac lumen is clear and the boundary is neat. The Entropy value that can be achieved by our method framework (4D-NLM+ACEHP) is 4.84. Conclusions Our new framework can thus provide important improvements to clinical fetal heart ultrasound images.
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Affiliation(s)
- Bin Liu
- International School of Information Science & Engineering (DUT-RUISE), Dalian University of Technology, Dalian, China.,Key Lab of Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian, China.,DUT-RU Co-Research Center of Advanced ICT for Active Life, Dalian University of Technology, Dalian, China
| | - Zhao Xu
- International School of Information Science & Engineering (DUT-RUISE), Dalian University of Technology, Dalian, China
| | - Qifeng Wang
- International School of Information Science & Engineering (DUT-RUISE), Dalian University of Technology, Dalian, China
| | - Xiaolei Niu
- International School of Information Science & Engineering (DUT-RUISE), Dalian University of Technology, Dalian, China
| | - Wei Xuan Chan
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Wiputra Hadi
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Choon Hwai Yap
- Department of Biomedical Engineering, National University of Singapore, Singapore.,Department of Bioengineering, Imperial College London, UK
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Lupsor-Platon M, Serban T, Silion AI, Tirpe GR, Tirpe A, Florea M. Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease. Cancers (Basel) 2021; 13:790. [PMID: 33672827 PMCID: PMC7918928 DOI: 10.3390/cancers13040790] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 12/14/2020] [Accepted: 02/09/2021] [Indexed: 12/12/2022] Open
Abstract
Global statistics show an increasing percentage of patients that develop non-alcoholic fatty liver disease (NAFLD) and NAFLD-related hepatocellular carcinoma (HCC), even in the absence of cirrhosis. In the present review, we analyzed the diagnostic performance of ultrasonography (US) in the non-invasive evaluation of NAFLD and NAFLD-related HCC, as well as possibilities of optimizing US diagnosis with the help of artificial intelligence (AI) assistance. To date, US is the first-line examination recommended in the screening of patients with clinical suspicion of NAFLD, as it is readily available and leads to a better disease-specific surveillance. However, the conventional US presents limitations that significantly hamper its applicability in quantifying NAFLD and accurately characterizing a given focal liver lesion (FLL). Ultrasound contrast agents (UCAs) are an essential add-on to the conventional B-mode US and to the Doppler US that further empower this method, allowing the evaluation of the enhancement properties and the vascular architecture of FLLs, in comparison to the background parenchyma. The current paper also explores the new universe of AI and the various implications of deep learning algorithms in the evaluation of NAFLD and NAFLD-related HCC through US methods, concluding that it could potentially be a game changer for patient care.
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Affiliation(s)
- Monica Lupsor-Platon
- Medical Imaging Department, Regional Institute of Gastroenterology and Hepatology, Iuliu Hatieganu University of Medicine and Pharmacy, 400162 Cluj-Napoca, Romania
| | - Teodora Serban
- Medical Imaging Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400162 Cluj-Napoca, Romania; (T.S.); (A.I.S.)
| | - Alexandra Iulia Silion
- Medical Imaging Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400162 Cluj-Napoca, Romania; (T.S.); (A.I.S.)
| | - George Razvan Tirpe
- County Emergency Hospital Cluj-Napoca, 3-5 Clinicilor Street, 400000 Cluj-Napoca, Romania;
| | - Alexandru Tirpe
- Research Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 23 Marinescu Street, 400337 Cluj-Napoca, Romania;
| | - Mira Florea
- Community Medicine Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400001 Cluj-Napoca, Romania;
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Chen JR, Chao YP, Tsai YW, Chan HJ, Wan YL, Tai DI, Tsui PH. Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1006. [PMID: 33286775 PMCID: PMC7597079 DOI: 10.3390/e22091006] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 08/31/2020] [Accepted: 09/07/2020] [Indexed: 02/07/2023]
Abstract
Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (p < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis.
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Affiliation(s)
- Jheng-Ru Chen
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (Y.-W.T.); (H.-J.C.); (Y.-L.W.)
| | - Yi-Ping Chao
- Department of Computer Science and Information Engineering, College of Engineering, Chang Gung University, Taoyuan 333323, Taiwan;
- Graduate Institute of Biomedical Engineering, Chang Gung University, College of Engineering, Taoyuan 333323, Taiwan
- Department of Neurology, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
| | - Yu-Wei Tsai
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (Y.-W.T.); (H.-J.C.); (Y.-L.W.)
| | - Hsien-Jung Chan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (Y.-W.T.); (H.-J.C.); (Y.-L.W.)
| | - Yung-Liang Wan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (Y.-W.T.); (H.-J.C.); (Y.-L.W.)
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
| | - Dar-In Tai
- Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan 333423, Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333323, Taiwan; (J.-R.C.); (Y.-W.T.); (H.-J.C.); (Y.-L.W.)
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 333423, Taiwan
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Song KD. Current status of deep learning applications in abdominal ultrasonography. Ultrasonography 2020; 40:177-182. [PMID: 33242931 PMCID: PMC7994733 DOI: 10.14366/usg.20085] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 09/02/2020] [Indexed: 12/12/2022] Open
Abstract
Deep learning is one of the most popular artificial intelligence techniques used in the medical field. Although it is at an early stage compared to deep learning analyses of computed tomography or magnetic resonance imaging, studies applying deep learning to ultrasound imaging have been actively conducted. This review analyzes recent studies that applied deep learning to ultrasound imaging of various abdominal organs and explains the challenges encountered in these applications.
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Affiliation(s)
- Kyoung Doo Song
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Liu Q, Nie H, Xu H, Wang P, Lei P. Noninvasive Imaging for the Evaluation of Non-Alcoholic Fatty Liver Disease Spectrum. Korean J Radiol 2020; 22:155-158. [PMID: 32767866 PMCID: PMC7772382 DOI: 10.3348/kjr.2020.0492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 04/18/2020] [Accepted: 04/24/2020] [Indexed: 11/23/2022] Open
Affiliation(s)
- Qianjiao Liu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Hongfang Nie
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Hengtian Xu
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Pingxian Wang
- Department of Medical Insurance, The Affiliated Hospital of Guizhou Medical University, Guiyang, China
| | - Pinggui Lei
- Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, China.
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Park HJ, Park B, Lee SS. Radiomics and Deep Learning: Hepatic Applications. Korean J Radiol 2020; 21:387-401. [PMID: 32193887 PMCID: PMC7082656 DOI: 10.3348/kjr.2019.0752] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Accepted: 01/05/2020] [Indexed: 12/12/2022] Open
Abstract
Radiomics and deep learning have recently gained attention in the imaging assessment of various liver diseases. Recent research has demonstrated the potential utility of radiomics and deep learning in staging liver fibroses, detecting portal hypertension, characterizing focal hepatic lesions, prognosticating malignant hepatic tumors, and segmenting the liver and liver tumors. In this review, we outline the basic technical aspects of radiomics and deep learning and summarize recent investigations of the application of these techniques in liver disease.
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Affiliation(s)
- Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Bumwoo Park
- Health Innovation Big Data Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea
| | - Seung Soo Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Reimer KC, Wree A, Roderburg C, Tacke F. New drugs for NAFLD: lessons from basic models to the clinic. Hepatol Int 2019; 14:8-23. [PMID: 31802390 DOI: 10.1007/s12072-019-10001-4] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 11/01/2019] [Indexed: 12/14/2022]
Abstract
The term nonalcoholic fatty liver disease (NAFLD) comprises a spectrum of increasingly harmful conditions ranging from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) to liver fibrosis and end-stage cirrhosis. NAFLD is the currently most common form of chronic liver disease in both adults and children worldwide. As NAFLD evolves as a global pandemic alongside the still growing prevalence of metabolic syndrome, obesity, and diabetes, it is inevitable to develop effective counterstrategies. Over the last decades, great effort has been dedicated to the understanding of the pathogenesis of NAFLD. This includes the development of an array of models for NAFLD, ranging from advanced in vitro (primary cells, 3D cultures, biochip, spheroids, organoids) to in vivo rodent models (particularly in mice). Based on these approaches novel therapies have been proposed and subsequently evaluated for patients with advanced forms of NAFLD, in particular those with NASH and liver fibrosis or cirrhosis. In this review, we delineate the current understanding of disease pathophysiology and depict how novel therapeutic strategies aim to exploit these different mechanisms to ameliorate, treat, or stop progression of NASH. We also discuss obstacles and chances along the way from basic models to promising clinical treatment options.
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Affiliation(s)
- Katharina C Reimer
- Department of Medicine II, Nephrology/Rheumatology/Clinical Immunology, University Hospital RWTH Aachen, 52074, Aachen, Germany
| | - Alexander Wree
- Department of Hepatology and Gastroenterology, Charité University Medical Center, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Christoph Roderburg
- Department of Hepatology and Gastroenterology, Charité University Medical Center, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Frank Tacke
- Department of Hepatology and Gastroenterology, Charité University Medical Center, Augustenburger Platz 1, 13353, Berlin, Germany.
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