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Ni JK, Ling ZL, Liang X, Song YH, Zhang GM, Chen CX, Wang LM, Wang P, Li GC, Ma SY, Gao J, Chang L, Zhang XX, Zhong N, Li Z. A convolutional neural network-based system for identifying neuroendocrine neoplasms and multiple types of lesions in the pancreas using EUS (with videos). Gastrointest Endosc 2025; 101:1020-1029.e3. [PMID: 39424005 DOI: 10.1016/j.gie.2024.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 10/04/2024] [Accepted: 10/08/2024] [Indexed: 10/21/2024]
Abstract
BACKGROUND AND AIMS EUS is sensitive in detecting pancreatic neuroendocrine neoplasm (pNEN). However, the endoscopic diagnosis of pNEN is operator-dependent and time-consuming because pNEN mimics normal pancreas and other pancreatic lesions. We intended to develop a convolutional neural network (CNN)-based system, named iEUS, for identifying pNEN and multiple types of pancreatic lesions using EUS. METHODS Retrospective data of 12,200 EUS images obtained from pNEN and non-pNEN pancreatic lesions, including pancreatic ductal adenocarcinoma (PDAC), autoimmune pancreatitis (AIP), and pancreatic cystic neoplasm (PCN), were used to develop iEUS, which was composed of a 2-category (pNEN or non-pNEN pancreatic lesions) classification model (CNN1) and a 4-category (pNEN, PDAC, AIP, or PCN) classification model (CNN2). Videos from consecutive patients were prospectively collected for a human-iEUS contest to evaluate the performance of iEUS. RESULTS Five hundred seventy-three patients were enrolled in this study. In the human-iEUS contest containing 203 videos, CNN1 and CNN2 showed an accuracy of 84.2% and 88.2% for diagnosing pNEN, respectively, which were significantly higher than that of novices (75.4%) and comparable with intermediate endosonographers (85.5%) and experts (85.5%). In addition, CNN2 showed an accuracy of 86.2%, 97.0%, and 97.0% for diagnosing PDAC, AIP, and PCN, respectively. With the assistance of iEUS, the sensitivity of endosonographers at all 3 levels in diagnosing pNEN has significantly improved (64.6% vs 44.8%, 87.5% vs 71.9%, and 74.0% vs 57.6%, respectively). CONCLUSIONS The iEUS precisely diagnosed pNEN and other confusing pancreatic lesions and thus can assist endosonographers in achieving more accessible and accurate endoscopic diagnoses with EUS. (Clinical trial registration number: ChiCTR2100049697.).
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Affiliation(s)
- Jie-Kun Ni
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Digestive Disease, Shandong, China; Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Ze-Le Ling
- Shandong Flag Information Technology Co, LTD, Shandong, China
| | - Xiao Liang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Digestive Disease, Shandong, China; Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Yi-Hao Song
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Digestive Disease, Shandong, China; Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Guo-Ming Zhang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Digestive Disease, Shandong, China; Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Chang-Xu Chen
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Digestive Disease, Shandong, China; Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Li-Mei Wang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Digestive Disease, Shandong, China; Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Peng Wang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Digestive Disease, Shandong, China; Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Guang-Chao Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Digestive Disease, Shandong, China; Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Shi-Yang Ma
- Division of Gastroenterology, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Jun Gao
- Department of Gastroenterology, Sunshine Union Hospital, Weifang, China
| | - Le Chang
- Department of Gastroenterology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Shanxi, China
| | - Xin-Xin Zhang
- Shandong Flag Information Technology Co, LTD, Shandong, China
| | - Ning Zhong
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Digestive Disease, Shandong, China; Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Shandong Provincial Clinical Research Center for Digestive Disease, Shandong, China; Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China; Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, China
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Osagiede O, Wallace MB. The Role of Artificial Intelligence for Advanced Endoscopy. Gastrointest Endosc Clin N Am 2025; 35:419-430. [PMID: 40021238 DOI: 10.1016/j.giec.2024.10.006] [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: 03/03/2025]
Abstract
Artificial intelligence (AI) application in gastroenterology has grown in the last decade and continues to evolve very rapidly. Early promising results have opened the door to explore its potential application to advanced endoscopy (AE). The aim of this review is to discuss the current state of the art and future directions of AI in AE. Current evidence suggests that AI-assisted endoscopic ultrasound models can be used in clinical practice to distinguish between benign and malignant pancreatic diseases with excellent results. AI-assisted endoscopic retrograde cholangiopancreatography models could also be useful in identifying the papilla, predicting difficult cannulation, and differentiating between benign and malignant strictures.
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Affiliation(s)
- Osayande Osagiede
- Division of Gastroenterology and Hepatology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL 32224, USA.
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL 32224, USA
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Carrara S, Andreozzi M, Terrin M, Spadaccini M. Role of Artificial Intelligence for Endoscopic Ultrasound. Gastrointest Endosc Clin N Am 2025; 35:407-418. [PMID: 40021237 DOI: 10.1016/j.giec.2024.10.007] [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: 03/03/2025]
Abstract
Endoscopic ultrasound (EUS) is widely used for the diagnosis of biliopancreatic and gastrointestinal tract diseases, but it is one of the most operator-dependent endoscopic techniques, requiring a long and complex learning curve. The role of artificial intelligence (AI) in EUS is growing as AI algorithms can assist in lesion detection and characterization by analyzing EUS images. Deep learning (DL) techniques, such as convolutional neural networks, have shown great potential for tumor identification; the application of AI models can increase the EUS diagnostic accuracy, provide faster diagnoses, and provide more information that can be helpful also for a training program.
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Affiliation(s)
- Silvia Carrara
- Gastroenterology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy.
| | - Marta Andreozzi
- Gastroenterology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy
| | - Maria Terrin
- Gastroenterology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy
| | - Marco Spadaccini
- Gastroenterology Department, IRCCS Humanitas Research Hospital, Via Manzoni 56, Rozzano, Milan 20089, Italy; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, Milan 20072, Italy
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Zhang YH, Fang AQ, Zhu HY, Du YQ. Facing the challenges of autoimmune pancreatitis diagnosis: The answer from artificial intelligence. World J Gastroenterol 2025; 31:102950. [PMID: 40182594 PMCID: PMC11962844 DOI: 10.3748/wjg.v31.i12.102950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Revised: 02/19/2025] [Accepted: 02/26/2025] [Indexed: 03/26/2025] Open
Abstract
Current diagnosis of autoimmune pancreatitis (AIP) is challenging and often requires combining multiple dimensions. There is a need to explore new methods for diagnosing AIP. The development of artificial intelligence (AI) is evident, and it is believed to have potential in the clinical diagnosis of AIP. This article aims to list the current diagnostic difficulties of AIP, describe existing AI applications, and suggest directions for future AI usages in AIP diagnosis.
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Affiliation(s)
- You-Han Zhang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai 200433, China
| | - Ai-Qiao Fang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai 200433, China
| | - Hui-Yun Zhu
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai 200433, China
| | - Yi-Qi Du
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai 200433, China
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Fang D, Huang Y, Li S, Shi C, Bao J, Du D, Xuan L, Ye L, Zhang Y, Zhu C, Zheng H, Shi Z, Mei Q, Wang H. A semi-supervised convolutional neural network for diagnosis of pancreatic ductal adenocarcinoma based on EUS-FNA cytological images. BMC Cancer 2025; 25:495. [PMID: 40102799 PMCID: PMC11917044 DOI: 10.1186/s12885-025-13910-w] [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: 11/17/2024] [Accepted: 03/11/2025] [Indexed: 03/20/2025] Open
Abstract
BACKGROUND The cytological diagnostic process of EUS-FNA smears is time-consuming and manpower-intensive, and the conclusion could be subjective and controversial. Moreover, the relative lack of cytopathologists has limited the widespread implementation of Rapid on-site evaluation (ROSE) presently. Therefore, this study aimed to establish an AI system for the detection of pancreatic ductal adenocarcinoma (PDAC) based on EUS-FNA cytological images. METHODS We collected 3213 unified magnification images of pancreatic cell clusters from 210 pancreatic mass patients who underwent EUS-FNA in four hospitals. A semi-supervised CNN (SSCNN) system was developed to distinguish PDAC from Non-PDAC. The internal and external verifications were adopted and the diagnostic accuracy was compared between different seniorities of cytopathologists. 33 images of "Atypical" diagnosed by expert cytopathologists were selected to analyze the consistency between the system and definitive diagnosis. RESULTS The segmentation indicators Mean Intersection over Union (mIou), precision, recall and F1-score of SSCNN in internal and external testing sets were 88.3%, 93.21%,94.24%, 93.68% and 87.75%, 93.81%, 93.14%, 93.48% successively. The PDAC classification indicators of the SSCNN model including area under the ROC curve (AUC), accuracy, sensitivity, specificity, PPV and NPV in the internal testing set were 0.97%, 0.95%, 0.94%, 0.97%, 0.98%, 0.91% respectively, and 0.99%, 0.94%, 0.94%, 0.95%, 0.99%, 0.75% correspondingly in the external testing set. The diagnostic accuracy of senior, intermediate and junior cytopathologists was 95.00%, 88.33% and 76.67% under the binary diagnostic criteria of PDAC and non-PDAC. In comparison, the accuracy of the SSCNN system was 90.00% in the dataset of man-machine competition. The accuracy of the SSCNN model was highly consistent with senior cytopathologists (Kappa = 0.853, P = 0.001). The accuracy, sensitivity and specificity of the system in the classification of "atypical" cases were 78.79%, 84.20% and 71.43% respectively. CONCLUSION Not merely tremendous preparatory work was drastically reduced, the semi-supervised CNN model could effectively identify PDAC cell clusters in EUS-FNA cytological smears which achieved analogically diagnostic capability compared with senior cytopathologists, and showed outstanding performance in assisting to categorize "atypical" cases where manual diagnosis is controversial. TRIAL REGISTRATION This study was registered on clinicaltrials.gov, and its unique Protocol ID was PJ-2018-12-17.
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Affiliation(s)
- Dong Fang
- Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China
- Department of Gastroenterology, The Second People's Hospital of Hefei, Hefei, 230011, Anhui Province, China
| | - Yigeng Huang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, Anhui Province, China
- University of Science and Technology of China, Hefei, 230026, Anhui Province, China
| | - Suwen Li
- Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China
| | - Chen Shi
- Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China
| | - Junjun Bao
- Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China
| | - Dandan Du
- Department of Cytopathology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China
| | - Lanlan Xuan
- Department of Pathology, Anqing Hospital Affiliated to Anhui Medical University, Anqing, 246004, Anhui Province, China
| | - Leping Ye
- Department of Gastroenterology, Anqing Hospital Affiliated to Anhui Medical University, Anqing, 246004, Anhui Province, China
| | - Yanping Zhang
- Department of Gastroenterology, Anqing Hospital Affiliated to Anhui Medical University, Anqing, 246004, Anhui Province, China
| | - ChengLin Zhu
- Department of Biliary and Pancreatic Surgery, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, Anhui, China
| | - Hailun Zheng
- Department of Gastroenterology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, Anhui Province, China
| | - Zhenwang Shi
- Department of Gastroenterology, The Second People's Hospital of Hefei, Hefei, 230011, Anhui Province, China
| | - Qiao Mei
- Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China.
| | - Huanqin Wang
- Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, Anhui Province, China.
- University of Science and Technology of China, Hefei, 230026, Anhui Province, China.
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Yi N, Mo S, Zhang Y, Jiang Q, Wang Y, Huang C, Qin S, Jiang H. An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer. Sci Rep 2025; 15:3383. [PMID: 39870667 PMCID: PMC11772604 DOI: 10.1038/s41598-024-84749-7] [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: 08/27/2024] [Accepted: 12/26/2024] [Indexed: 01/29/2025] Open
Abstract
To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was employed to reduce the dimensionality of deep learning (DL) features extracted from pre-standardized EUS images. The retained nonzero coefficient features were subsequently applied to develop predictive eight DL models based on distinct machine learning algorithms. The optimal DL model was identified and used to establish a clinical signature, which subsequently informed the construction and evaluation of a nomogram. Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) were implemented to interpret and visualize the model outputs. A total of 2048 DL features were initially extracted, from which only 27 features with coefficients greater than zero were retained. The support vector machine (SVM) DL model demonstrated exceptional performance, achieving area under the curve (AUC) values of 0.948 and 0.795 in the training and test groups, respectively. Additionally, a nomogram was developed, incorporating both DL and clinical signatures, and was visually represented for practical application. Finally, the calibration curves, decision curve analysis (DCA) plots, and clinical impact curves (CIC) exhibited by the DL model and nomogram indicated high accuracy. The application of Grad-CAM and SHAP enhanced the interpretability of these models. These methodologies contributed substantial net benefits to clinical decision-making processes. A novel interpretable DL model and nomogram were developed and validated using EUS images, cooperating with machine learning algorithms. This approach demonstrates significant potential for enhancing the clinical applicability of EUS in predicting PNETs from pancreatic cancer, thereby offering valuable insights for future research and implementation.
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Affiliation(s)
- Nan Yi
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Shuangyang Mo
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
- Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Yan Zhang
- The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
| | - Qi Jiang
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yingwei Wang
- Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Cheng Huang
- Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Shanyu Qin
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
| | - Haixing Jiang
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
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Jain A, Pabba M, Jain A, Singh S, Ali H, Vinayek R, Aswath G, Sharma N, Inamdar S, Facciorusso A. Impact of Artificial Intelligence on Pancreaticobiliary Endoscopy. Cancers (Basel) 2025; 17:379. [PMID: 39941748 PMCID: PMC11815774 DOI: 10.3390/cancers17030379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2024] [Revised: 01/20/2025] [Accepted: 01/23/2025] [Indexed: 02/16/2025] Open
Abstract
Pancreaticobiliary diseases can lead to significant morbidity and their diagnoses rely on imaging and endoscopy which are dependent on operator expertise. Artificial intelligence (AI) has seen a rapid uptake in the field of luminal endoscopy, such as polyp detection during colonoscopy. However, its use for pancreaticobiliary endoscopic modalities such as endoscopic ultrasound (EUS) and cholangioscopy remains scarce, with only few studies available. In this review, we delve into the current evidence, benefits, limitations, and future scope of AI technologies in pancreaticobiliary endoscopy.
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Affiliation(s)
- Aryan Jain
- Department of Gastroenterology, Albany Medical College, Albany, NY 12208, USA; (A.J.); (M.P.); (A.J.)
| | - Mayur Pabba
- Department of Gastroenterology, Albany Medical College, Albany, NY 12208, USA; (A.J.); (M.P.); (A.J.)
| | - Aditya Jain
- Department of Gastroenterology, Albany Medical College, Albany, NY 12208, USA; (A.J.); (M.P.); (A.J.)
| | - Sahib Singh
- Department of Internal Medicine, Sinai Hospital of Baltimore, Baltimore, MD 21215, USA
| | - Hassam Ali
- Department of Gastroenterology, ECU Health Medical Center/Brody School of Medicine, Greenville, NC 27834, USA;
| | - Rakesh Vinayek
- Department of Gastroenterology, Sinai Hospital of Baltimore, Baltimore, MD 21215, USA;
| | - Ganesh Aswath
- Department of Gastroenterology, State University of New York Upstate Medical University, Syracuse, NY 13210, USA;
| | - Neil Sharma
- Department of Gastroenterology, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Sumant Inamdar
- Department of Gastroenterology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Experimental Medicine, University of Salento, 73100 Lecce, Italy;
- Clinical Effectiveness Research Group, Faculty of Medicine, Institute of Health and Society, University of Oslo, 0373 Oslo, Norway
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Onishi S, Kuwahara T, Tajika M, Tanaka T, Yamada K, Shimizu M, Niwa Y, Yamaguchi R. Artificial intelligence for body composition assessment focusing on sarcopenia. Sci Rep 2025; 15:1324. [PMID: 39779762 PMCID: PMC11711400 DOI: 10.1038/s41598-024-83401-8] [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: 09/06/2024] [Accepted: 12/13/2024] [Indexed: 01/11/2025] Open
Abstract
This study aimed to address the limitations of conventional methods for measuring skeletal muscle mass for sarcopenia diagnosis by introducing an artificial intelligence (AI) system for direct computed tomography (CT) analysis. The primary focus was on enhancing simplicity, reproducibility, and convenience, and assessing the accuracy and speed of AI compared with conventional methods. A cohort of 3096 cases undergoing CT imaging up to the third lumbar (L3) level between 2011 and 2021 were included. Random division into preprocessing and sarcopenia cohorts was performed, with further random splits into training and validation cohorts for BMI_AI and Body_AI creation. Sarcopenia_AI utilizes the Skeletal Muscle Index (SMI), which is calculated as (total skeletal muscle area at L3)/(height)2. The SMI was conventionally measured twice, with the first as the AI label reference and the second for comparison. Agreement and diagnostic change rates were calculated. Three groups were randomly assigned and 10 images before and after L3 were collected for each case. AI models for body region detection (Deeplabv3) and sarcopenia diagnosis (EfficientNetV2-XL) were trained on a supercomputer, and their abilities and speed per image were evaluated. The conventional method showed a low agreement rate (κ coefficient) of 0.478 for the test cohort and 0.236 for the validation cohort, with diagnostic changes in 43% of cases. Conversely, the AI consistently produced identical results after two measurements. The AI demonstrated robust body region detection ability (intersection over Union (IoU) = 0.93), accurately detecting only the body region in all images. The AI for sarcopenia diagnosis exhibited high accuracy, with a sensitivity of 82.3%, specificity of 98.1%, and a positive predictive value of 89.5%. In conclusion, the reproducibility of the conventional method for sarcopenia diagnosis was low. The developed sarcopenia diagnostic AI, with its high positive predictive value and convenient diagnostic capabilities, is a promising alternative for addressing the shortcomings of conventional approaches.
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Affiliation(s)
- Sachiyo Onishi
- Department of Endoscopy, Aichi Cancer Center, Nagoya, Aichi, Japan
| | - Takamichi Kuwahara
- Department of Gastroenterology, Aichi Cancer Center, 1-1 Kanokoden, Chikusa-ku, Nagoya, Aichi, 464-8681, Japan.
| | - Masahiro Tajika
- Department of Endoscopy, Aichi Cancer Center, Nagoya, Aichi, Japan
| | - Tsutomu Tanaka
- Department of Endoscopy, Aichi Cancer Center, Nagoya, Aichi, Japan
| | - Keisaku Yamada
- Department of Endoscopy, Aichi Cancer Center, Nagoya, Aichi, Japan
| | - Masahito Shimizu
- Department of Gastroenterology/Internal Medicine, Gifu University School of Medicine Graduate School of Medicine, Gifu, Gifu, Japan
| | - Yasumasa Niwa
- Department of Endoscopy, Aichi Cancer Center, Nagoya, Aichi, Japan
| | - Rui Yamaguchi
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Nagoya, Aichi, Japan
- Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Konikoff T, Loebl N, Benson AA, Green O, Sandler H, Gingold-Belfer R, Levi Z, Perl L, Dotan I, Shamah S. Enhancing detection of various pancreatic lesions on endoscopic ultrasound through artificial intelligence: a basis for computer-aided detection systems. J Gastroenterol Hepatol 2025; 40:235-240. [PMID: 39538430 DOI: 10.1111/jgh.16814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 10/24/2024] [Accepted: 10/31/2024] [Indexed: 11/16/2024]
Abstract
BACKGROUND AND AIM Endoscopic ultrasound (EUS) is the most sensitive method for evaluation of pancreatic lesions but is limited by significant operator dependency. Artificial intelligence (AI), in the form of computer-aided detection (CADe) systems, has shown potential in increasing accuracy and bridging operator dependency in several endoscopic domains. However, the complexity of integrating AI into EUS is far more challenging. This aims to develop and test the basis for a CADe system for real-time detection and segmentation of all pancreatic lesions. METHODS In this single-center study EUS studies of pancreatic findings were included. Lesions were outlined by two expert (>5 years performing EUS) endoscopists, and the two leading types of models were benchmarked. The models' performance was evaluated through per-pixel intersection over union (IoU). RESULTS A total of 1497 EUS images from 165 patients were evaluated. The dataset included malignancies, neuroendocrine tumors, benign cysts, chronic and acute pancreatitis, normal fatty pancreas, and benign lesions. The best model demonstrated detection and segmentation on the test set with a mean IoU of 0.73, achieving a PPV, NPV, total accuracy, and ROC of 0.82, 0.96, 0.95, and 0.95, respectively. The algorithm is adaptable for real-time processing. CONCLUSIONS We developed and tested deep learning models for real-time detection and segmentation of pancreatic lesions during EUS with promising results. This constitutes the basis for a CADe system for EUS, which could be valuable in future detection and evaluation of pancreatic lesions. Further studies for validation and generalization are underway.
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Affiliation(s)
- Tom Konikoff
- Division of Gastroenterology, Rabin Medical Center, Petach-Tikva, Israel
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Nadav Loebl
- Rabin Medical Center Innovation Lab, Rabin Medical Center, Petah Tikva, Israel
| | - Ariel A Benson
- Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Institute of Gastroenterology and Liver Diseases, Jerusalem, Israel
| | - Orr Green
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Hunter Sandler
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Rachel Gingold-Belfer
- Division of Gastroenterology, Rabin Medical Center, Petach-Tikva, Israel
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Zohar Levi
- Division of Gastroenterology, Rabin Medical Center, Petach-Tikva, Israel
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Leor Perl
- Rabin Medical Center Innovation Lab, Rabin Medical Center, Petah Tikva, Israel
| | - Iris Dotan
- Division of Gastroenterology, Rabin Medical Center, Petach-Tikva, Israel
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Steven Shamah
- Division of Gastroenterology, Rabin Medical Center, Petach-Tikva, Israel
- Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel
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Rai HM, Yoo J, Razaque A. Comparative analysis of machine learning and deep learning models for improved cancer detection: A comprehensive review of recent advancements in diagnostic techniques. EXPERT SYSTEMS WITH APPLICATIONS 2024; 255:124838. [DOI: 10.1016/j.eswa.2024.124838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
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Loper MR, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging. Tomography 2024; 10:1814-1831. [PMID: 39590942 PMCID: PMC11598375 DOI: 10.3390/tomography10110133] [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: 09/08/2024] [Revised: 11/11/2024] [Accepted: 11/15/2024] [Indexed: 11/28/2024] Open
Abstract
Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.
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Affiliation(s)
| | - Mina S. Makary
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA;
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Agudo Castillo B, Mascarenhas M, Martins M, Mendes F, de la Iglesia D, Costa AMMPD, Esteban Fernández-Zarza C, González-Haba Ruiz M. Advancements in biliopancreatic endoscopy - A comprehensive review of artificial intelligence in EUS and ERCP. REVISTA ESPANOLA DE ENFERMEDADES DIGESTIVAS 2024; 116:613-622. [PMID: 38832589 DOI: 10.17235/reed.2024.10456/2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.
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Affiliation(s)
| | | | - Miguel Martins
- Gastroenterology, Centro Hospitalar Universitário de São João
| | - Francisco Mendes
- Gastroenterology, Centro Hospitalar Universitário de São João, Portugal
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Hu SS, Duan B, Xu L, Huang D, Liu X, Gou S, Zhao X, Hou J, Tan S, He LY, Ye Y, Xie X, Shen H, Liu WH. Enhancing physician support in pancreatic cancer diagnosis: New M-F-RCNN artificial intelligence model using endoscopic ultrasound. Endosc Int Open 2024; 12:E1277-E1284. [PMID: 39524196 PMCID: PMC11543282 DOI: 10.1055/a-2422-9214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Accepted: 09/26/2024] [Indexed: 11/16/2024] Open
Abstract
Background and study aims Endoscopic ultrasound (EUS) is vital for early pancreatic cancer diagnosis. Advances in artificial intelligence (AI), especially deep learning, have improved medical image analysis. We developed and validated the Modified Faster R-CNN (M-F-RCNN), an AI algorithm using EUS images to assist in diagnosing pancreatic cancer. Methods We collected EUS images from 155 patients across three endoscopy centers from July 2022 to July 2023. M-F-RCNN development involved enhancing feature information through data preprocessing and utilizing an improved Faster R-CNN model to identify cancerous regions. Its diagnostic capabilities were validated against an external set of 1,000 EUS images. In addition, five EUS doctors participated in a study comparing the M-F-RCNN model's performance with that of human experts, assessing diagnostic skill improvements with AI assistance. Results Internally, the M-F-RCNN model surpassed traditional algorithms with an average precision of 97.35%, accuracy of 96.49%, and recall rate of 5.44%. In external validation, its sensitivity, specificity, and accuracy were 91.7%, 91.5%, and 91.6%, respectively, outperforming non-expert physicians. The model also significantly enhanced the diagnostic skills of doctors. Conclusions: The M-F-RCNN model shows exceptional performance in diagnosing pancreatic cancer via EUS images, greatly improving diagnostic accuracy and efficiency, thus enhancing physician proficiency and reducing diagnostic errors.
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Affiliation(s)
- Shan-shan Hu
- Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Bowen Duan
- Endoscopy, Sichuan Cancer Hospital and Institute, Chengdu, China
| | - Li Xu
- Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Danping Huang
- Engineering and Science, Sichuan University of Science and Engineering Artificial Intelligence Key Laboratory of Sichuan Province, Yibin, China
| | - Xiaogang Liu
- Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Shihao Gou
- Engineering and Science, Sichuan University of Science and Engineering Artificial Intelligence Key Laboratory of Sichuan Province, Yibin, China
| | - Xiaochen Zhao
- Hepatobiliary Pancreatic Surgery, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China
| | - Jie Hou
- Digestive Endoscopy Center of the Dongyuan, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China
| | - Shirong Tan
- Digestive Endoscopy Center of the Dongyuan, Sichuan Academy of Medical Sciences and Sichuan People's Hospital, Chengdu, China
| | - lan ying He
- Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China
| | - Ying Ye
- Department of Gastroenterology, Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital, Chengdu, China
| | - Xiaoli Xie
- Gastroenterology, The First People's Hospital of Longquanyi District Chengdu, Chengdu, China
| | - Hong Shen
- Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Wei-hui Liu
- Department of Gastroenterology and Hepatology, Sichuan Provincial Peopleʼs Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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Gong J, Zhang Q, Peng Q, Shi D. Identification of Chronic Pancreatitis Associated microRNAs and Genes for the Diagnosis of Pancreatic Cancer. Am Surg 2024; 90:2797-2807. [PMID: 38708574 DOI: 10.1177/00031348241253801] [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: 05/07/2024]
Abstract
OBJECTIVE The timely identification of both malignant and nonmalignant pancreatic lesions has the potential to significantly enhance prognosis and implement risk management strategies across various levels. microRNAs (miRs) and their corresponding targets play a crucial role in the development of pancreatic lesions and can serve as valuable diagnostic and therapeutic targets. The objective of our study was to investigate potential diagnostic markers that can effectively differentiate between malignant and nonmalignant pancreatic lesions. METHODS Gene Expression Omnibus (GEO) database with GSE24279 dataset was utilized to screen differentially expressed miRNAs (DEMs). We utilized the TargetScanHuman database to predict the target genes associated with hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p. Furthermore, a cohort comprising healthy individuals (n = 52), chronic pancreatitis (CP; n = 34), and pancreatic adenocarcinoma (PAAD; n = 53) patients was recruited to ascertain the levels of plasma markers. RESULTS We identified 3 miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and 2 proteins (PCDH1 and AMN) as potential diagnostic markers for distinguishing between CP and PAAD. The area under the curve (AUC) values for all markers exceeded .800. Notably, a combination of plasma PCDH1 and AMN demonstrated excellent diagnostic performance (AUC = .921; 95% CI: .866-.977; sensitivity = .792; specificity = .941) in discriminating between CP and PAAD. In addition, the model of hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p yielded an AUC of .928, sensitivity of .830, and specificity of .912, respectively. CONCLUSION Plasma levels of miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and their corresponding targets (PCDH1 and AMN) hold promise as potential biomarkers for predicting PAAD in patients with CP.
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Affiliation(s)
- Jing Gong
- Department of Medical Laboratory, Jiangxi Integrated Traditional Chinese and Western Medicine Hospital, Nanchang City, Jiangxi Province, China
| | - Qinghua Zhang
- Department of Medical Laboratory, Jiangxi Integrated Traditional Chinese and Western Medicine Hospital, Nanchang City, Jiangxi Province, China
| | - Qimin Peng
- Department of Medical Laboratory, Jiangxi Integrated Traditional Chinese and Western Medicine Hospital, Nanchang City, Jiangxi Province, China
| | - Dongling Shi
- Department of Health examination, Jiangxi Integrated traditional Chinese and Western Medicine Hospital, Nanchang City, Jiangxi Province, China
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Kurita Y, Utsunomiya D, Kubota K, Koyama S, Hasegawa S, Hosono K, Irie K, Suzuki Y, Maeda S, Kobayashi N, Ichikawa Y, Endo I, Nakajima A. Diagnostic Value of Contrast-Enhanced Dual-Energy Computed Tomography in the Pancreatic Parenchymal and Delayed Phases for Pancreatic Cancer. Tomography 2024; 10:1591-1604. [PMID: 39453034 PMCID: PMC11510840 DOI: 10.3390/tomography10100117] [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: 08/23/2024] [Revised: 09/28/2024] [Accepted: 10/03/2024] [Indexed: 10/26/2024] Open
Abstract
Background/Objectives: The usefulness of dual-energy computed tomography (DECT) for low absorption in the parenchymal phase and contrast effects in the delayed phase for pancreatic cancer is not clear. Therefore, the diagnostic capability of low-KeV images obtained using DECT for pancreatic cancer in the pancreatic parenchymal and delayed phases was evaluated quantitatively and qualitatively. Methods: Twenty-five patients with pancreatic cancer who underwent contrast-enhanced DECT were included. A total of 50 and 70 KeV CT images, classified as low-keV and conventional CT-equivalent images, were produced, respectively. The tumor-to-pancreas contrast (Hounsfield units [HU]) in the pancreatic parenchymal and delayed phases was calculated by subtracting the CT value of the pancreatic tumor from that of normal parenchyma. Results: The median tumor-to-pancreas contrast on 50 KeV CT in the pancreatic parenchymal phase (133 HU) was higher than that on conventional CT (68 HU) (p < 0.001). The median tumor-to-pancreas contrast in the delayed phase was -28 HU for 50 KeV CT and -9 HU for conventional CT (p = 0.545). For tumors < 20 mm, the tumor-to-pancreas contrast of 50 KeV CT (-39 HU) had a significantly clearer contrast effect than that of conventional CT (-16.5 HU), even in the delayed phase (p = 0.034). Conclusions: These 50 KeV CT images may clarify the low-absorption areas of pancreatic cancer in the pancreatic parenchymal phase. A good contrast effect was observed in small pancreatic cancers on 50 KeV delayed-phase images, suggesting that DECT is useful for the visualization of early pancreatic cancer with a small tumor diameter.
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Affiliation(s)
- Yusuke Kurita
- Department of Gastroenterology and Hepatology, Yokohama City University, Yokohama 236-0004, Japan; (K.K.); (S.H.); (K.H.); (A.N.)
| | - Daisuke Utsunomiya
- Department of Diagnostic Radiology, Graduate School of Medicine, Yokohama City University, Yokohama 236-0004, Japan; (D.U.); (S.K.)
| | - Kensuke Kubota
- Department of Gastroenterology and Hepatology, Yokohama City University, Yokohama 236-0004, Japan; (K.K.); (S.H.); (K.H.); (A.N.)
| | - Shingo Koyama
- Department of Diagnostic Radiology, Graduate School of Medicine, Yokohama City University, Yokohama 236-0004, Japan; (D.U.); (S.K.)
| | - Sho Hasegawa
- Department of Gastroenterology and Hepatology, Yokohama City University, Yokohama 236-0004, Japan; (K.K.); (S.H.); (K.H.); (A.N.)
| | - Kunihiro Hosono
- Department of Gastroenterology and Hepatology, Yokohama City University, Yokohama 236-0004, Japan; (K.K.); (S.H.); (K.H.); (A.N.)
| | - Kuniyasu Irie
- Department of Gastroenterology, Yokohama City University, Yokohama 236-0004, Japan; (K.I.); (Y.S.); (S.M.)
| | - Yuichi Suzuki
- Department of Gastroenterology, Yokohama City University, Yokohama 236-0004, Japan; (K.I.); (Y.S.); (S.M.)
| | - Shin Maeda
- Department of Gastroenterology, Yokohama City University, Yokohama 236-0004, Japan; (K.I.); (Y.S.); (S.M.)
| | - Noritoshi Kobayashi
- Department of Oncology, Yokohama City University, Yokohama 236-0004, Japan; (N.K.); (Y.I.)
| | - Yasushi Ichikawa
- Department of Oncology, Yokohama City University, Yokohama 236-0004, Japan; (N.K.); (Y.I.)
| | - Itaru Endo
- Department of Gastroenterological Surgery, Yokohama City University, Yokohama 236-0004, Japan;
| | - Atsushi Nakajima
- Department of Gastroenterology and Hepatology, Yokohama City University, Yokohama 236-0004, Japan; (K.K.); (S.H.); (K.H.); (A.N.)
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Martinez M, Bartel MJ, Chua T, Dakhoul L, Fatima H, Jensen D, Lara LF, Tadros M, Villa E, Yang D, Saltzman JR. The 2023 top 10 list of endoscopy topics in medical publishing: an annual review by the American Society for Gastrointestinal Endoscopy Editorial Board. Gastrointest Endosc 2024; 100:537-548. [PMID: 38729314 DOI: 10.1016/j.gie.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Accepted: 05/01/2024] [Indexed: 05/12/2024]
Abstract
Using a systematic literature search of original articles published during 2023 in Gastrointestinal Endoscopy (GIE) and other high-impact medical and gastroenterology journals, the GIE Editorial Board of the American Society for Gastrointestinal Endoscopy compiled a list of the top 10 most significant topic areas in general and advanced GI endoscopy during the year. Each GIE Editorial Board member was directed to consider 3 criteria in generating candidate topics-significance, novelty, and impact on global clinical practice-and subject matter consensus was facilitated by the Chair through electronic voting and a meeting of the entire GIE Editorial Board. The 10 identified areas collectively represent advances in the following endoscopic spheres: GI bleeding, endohepatology, endoscopic palliation, artificial intelligence and polyp detection, artificial intelligence beyond the colon, better polypectomy and EMR, how to make endoscopy units greener, high-quality upper endoscopy, endoscopic tissue apposition and closure devices, and endoscopic submucosal dissection. Each board member was assigned a topic area around which to summarize relevant important articles, thereby generating this overview of the "top 10" endoscopic advances of 2023.
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Affiliation(s)
- Melissa Martinez
- Digestive Health Institute, Carle Foundation Hospital, Urbana, Illinois, USA
| | | | - Tiffany Chua
- Department of Gastroenterology, Hepatology and Nutrition, University of Florida, Gainesville, Florida, USA
| | - Lara Dakhoul
- Department of Gastroenterology, Hepatology and Nutrition, University of Florida, Gainesville, Florida, USA
| | - Hala Fatima
- Department of Internal Medicine, Division of Gastroenterology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Dennis Jensen
- Ronald Reagan UCLA Medical Center and The VA Greater Los Angeles Healthcare System, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Luis F Lara
- Division of Digestive Diseases, University of Cincinnati, Cincinnati, Ohio, USA
| | - Michael Tadros
- Division of Gastroenterology, Albany Medical Center, Albany, New York, USA
| | | | - Dennis Yang
- Center of Interventional Endoscopy, Advent Health, Orlando, Florida, USA
| | - John R Saltzman
- Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Jiang H, Ye LS, Yuan XL, Luo Q, Zhou NY, Hu B. Artificial intelligence in pancreaticobiliary endoscopy: Current applications and future directions. J Dig Dis 2024; 25:564-572. [PMID: 39740251 DOI: 10.1111/1751-2980.13324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2024] [Revised: 11/13/2024] [Accepted: 12/03/2024] [Indexed: 01/02/2025]
Abstract
Pancreaticobiliary endoscopy is an essential tool for diagnosing and treating pancreaticobiliary diseases. However, it does not fully meet clinical needs, which presents challenges such as significant difficulty in operation and risks of missed diagnosis or misdiagnosis. In recent years, artificial intelligence (AI) has enhanced the diagnostic and treatment efficiency and quality of pancreaticobiliary endoscopy. Diagnosis and differential diagnosis based on endoscopic ultrasound (EUS) images, pathology of EUS-guided fine-needle aspiration or biopsy, need for endoscopic retrograde cholangiopancreatography (ERCP) and assessment of operational difficulty, postoperative complications and prediction of patient prognosis, and real-time procedure guidance. This review provides an overview of AI applications in pancreaticobiliary endoscopy and proposes future development directions in aspects such as data quality and algorithmic interpretability, aiming to provide new insights for the integration of AI technology with pancreaticobiliary endoscopy.
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Affiliation(s)
- Huan Jiang
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Lian Song Ye
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Xiang Lei Yuan
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Qi Luo
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Nuo Ya Zhou
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Digestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- Med-X Center for Materials, Sichuan University, Chengdu, Sichuan Province, China
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18
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Blackford AL, Canto MI, Dbouk M, Hruban RH, Katona BW, Chak A, Brand RE, Syngal S, Farrell J, Kastrinos F, Stoffel EM, Rustgi A, Klein AP, Kamel I, Fishman EK, He J, Burkhart R, Shin EJ, Lennon AM, Goggins M. Pancreatic Cancer Surveillance and Survival of High-Risk Individuals. JAMA Oncol 2024; 10:1087-1096. [PMID: 38959011 PMCID: PMC11223057 DOI: 10.1001/jamaoncol.2024.1930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/05/2024] [Indexed: 07/04/2024]
Abstract
Importance Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease with increasing incidence. The majority of PDACs are incurable at presentation, but population-based screening is not recommended. Surveillance of high-risk individuals for PDAC may lead to early detection, but the survival benefit is unproven. Objective To compare the survival of patients with surveillance-detected PDAC with US national data. Design, Setting, and Participants This comparative cohort study was conducted in multiple US academic medical centers participating in the Cancer of the Pancreas Screening program, which screens high-risk individuals with a familial or genetic predisposition for PDAC. The comparison cohort comprised patients with PDAC matched for age, sex, and year of diagnosis from the Surveillance, Epidemiology, and End Results (SEER) program. The Cancer of the Pancreas Screening program originated in 1998, and data collection was done through 2021. The data analysis was performed from April 29, 2022, through April 10, 2023. Exposures Endoscopic ultrasonography or magnetic resonance imaging performed annually and standard-of-care surgical and/or oncologic treatment. Main Outcomes and Measures Stage of PDAC at diagnosis, overall survival (OS), and PDAC mortality were compared using descriptive statistics and conditional logistic regression, Cox proportional hazards regression, and competing risk regression models. Sensitivity analyses and adjustment for lead-time bias were also conducted. Results A total of 26 high-risk individuals (mean [SD] age at diagnosis, 65.8 [9.5] years; 15 female [57.7%]) with PDAC were compared with 1504 SEER control patients with PDAC (mean [SD] age at diagnosis, 66.8 [7.9] years; 771 female [51.3%]). The median primary tumor diameter of the 26 high-risk individuals was smaller than in the control patients (2.5 [range, 0.6-5.0] vs 3.6 [range, 0.2-8.0] cm, respectively; P < .001). The high-risk individuals were more likely to be diagnosed with a lower stage (stage I, 10 [38.5%]; stage II, 8 [30.8%]) than matched control patients (stage I, 155 [10.3%]; stage II, 377 [25.1%]; P < .001). The PDAC mortality rate at 5 years was lower for high-risk individuals than control patients (43% vs 86%; hazard ratio, 3.58; 95% CI, 2.01-6.39; P < .001), and high-risk individuals lived longer than matched control patients (median OS, 61.7 [range, 1.9-147.3] vs 8.0 [range, 1.0-131.0] months; 5-year OS rate, 50% [95% CI, 32%-80%] vs 9% [95% CI, 7%-11%]). Conclusions and Relevance These findings suggest that surveillance of high-risk individuals may lead to detection of smaller, lower-stage PDACs and improved survival.
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Affiliation(s)
- Amanda L. Blackford
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Marcia Irene Canto
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Medicine (Gastroenterology), The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Mohamad Dbouk
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Ralph H. Hruban
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Bryson W. Katona
- Division of Gastroenterology, Department of Medicine, Abramson Cancer Center, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Amitabh Chak
- Division of Gastroenterology and Liver Disease, University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, Ohio
| | - Randall E. Brand
- Division of Gastroenterology, Hepatology and Nutrition, University of Pittsburgh Medical Center, Pennsylvania
| | - Sapna Syngal
- Cancer Genetics and Prevention, Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
- Division of Gastroenterology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - James Farrell
- Yale Center for Pancreatic Disease, Section of Digestive Disease, Yale University, New Haven, Connecticut
| | - Fay Kastrinos
- Division of Digestive and Liver Diseases, Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York
| | - Elena M. Stoffel
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan, Ann Arbor
| | - Anil Rustgi
- Division of Digestive and Liver Diseases, Herbert Irving Comprehensive Cancer Center, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, New York
| | - Alison P. Klein
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Ihab Kamel
- Department of Radiology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Elliot K. Fishman
- Department of Radiology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Jin He
- Department of Surgery, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Richard Burkhart
- Department of Surgery, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Eun Ji Shin
- Department of Medicine (Gastroenterology), The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Anne Marie Lennon
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Medicine (Gastroenterology), The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Radiology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Surgery, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
| | - Michael Goggins
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
- Department of Medicine (Gastroenterology), The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins Medical Institutions, Baltimore, Maryland
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Liu W, Zhang B, Liu T, Jiang J, Liu Y. Artificial Intelligence in Pancreatic Image Analysis: A Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:4749. [PMID: 39066145 PMCID: PMC11280964 DOI: 10.3390/s24144749] [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: 06/05/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 07/28/2024]
Abstract
Pancreatic cancer is a highly lethal disease with a poor prognosis. Its early diagnosis and accurate treatment mainly rely on medical imaging, so accurate medical image analysis is especially vital for pancreatic cancer patients. However, medical image analysis of pancreatic cancer is facing challenges due to ambiguous symptoms, high misdiagnosis rates, and significant financial costs. Artificial intelligence (AI) offers a promising solution by relieving medical personnel's workload, improving clinical decision-making, and reducing patient costs. This study focuses on AI applications such as segmentation, classification, object detection, and prognosis prediction across five types of medical imaging: CT, MRI, EUS, PET, and pathological images, as well as integrating these imaging modalities to boost diagnostic accuracy and treatment efficiency. In addition, this study discusses current hot topics and future directions aimed at overcoming the challenges in AI-enabled automated pancreatic cancer diagnosis algorithms.
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Affiliation(s)
- Weixuan Liu
- Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China; (W.L.); (B.Z.)
| | - Bairui Zhang
- Sydney Smart Technology College, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China; (W.L.); (B.Z.)
| | - Tao Liu
- School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China;
| | - Juntao Jiang
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yong Liu
- College of Control Science and Engineering, Zhejiang University, Hangzhou 310058, China
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20
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Cui H, Zhao Y, Xiong S, Feng Y, Li P, Lv Y, Chen Q, Wang R, Xie P, Luo Z, Cheng S, Wang W, Li X, Xiong D, Cao X, Bai S, Yang A, Cheng B. Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial. JAMA Netw Open 2024; 7:e2422454. [PMID: 39028670 PMCID: PMC11259905 DOI: 10.1001/jamanetworkopen.2024.22454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 05/16/2024] [Indexed: 07/21/2024] Open
Abstract
Importance Diagnosing solid lesions in the pancreas via endoscopic ultrasonographic (EUS) images is challenging. Artificial intelligence (AI) has the potential to help with such diagnosis, but existing AI models focus solely on a single modality. Objective To advance the clinical diagnosis of solid lesions in the pancreas through developing a multimodal AI model integrating both clinical information and EUS images. Design, Setting, and Participants In this randomized crossover trial conducted from January 1 to June 30, 2023, from 4 centers across China, 12 endoscopists of varying levels of expertise were randomly assigned to diagnose solid lesions in the pancreas with or without AI assistance. Endoscopic ultrasonographic images and clinical information of 439 patients from 1 institution who had solid lesions in the pancreas between January 1, 2014, and December 31, 2022, were collected to train and validate the joint-AI model, while 189 patients from 3 external institutions were used to evaluate the robustness and generalizability of the model. Intervention Conventional or AI-assisted diagnosis of solid lesions in the pancreas. Main Outcomes and Measures In the retrospective dataset, the performance of the joint-AI model was evaluated internally and externally. In the prospective dataset, diagnostic performance of the endoscopists with or without the AI assistance was compared. Results The retrospective dataset included 628 patients (400 men [63.7%]; mean [SD] age, 57.7 [27.4] years) who underwent EUS procedures. A total of 130 patients (81 men [62.3%]; mean [SD] age, 58.4 [11.7] years) were prospectively recruited for the crossover trial. The area under the curve of the joint-AI model ranged from 0.996 (95% CI, 0.993-0.998) in the internal test dataset to 0.955 (95% CI, 0.940-0.968), 0.924 (95% CI, 0.888-0.955), and 0.976 (95% CI, 0.942-0.995) in the 3 external test datasets, respectively. The diagnostic accuracy of novice endoscopists was significantly enhanced with AI assistance (0.69 [95% CI, 0.61-0.76] vs 0.90 [95% CI, 0.83-0.94]; P < .001), and the supplementary interpretability information alleviated the skepticism of the experienced endoscopists. Conclusions and Relevance In this randomized crossover trial of diagnosing solid lesions in the pancreas with or without AI assistance, the joint-AI model demonstrated positive human-AI interaction, which suggested its potential to facilitate a clinical diagnosis. Nevertheless, future randomized clinical trials are warranted. Trial Registration ClinicalTrials.gov Identifier: NCT05476978.
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Affiliation(s)
- Haochen Cui
- Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuchong Zhao
- Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Si Xiong
- Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yunlu Feng
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Peng Li
- Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ying Lv
- Department of Gastroenterology, Nanjing Drum Tower Hospital, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, China
| | - Qian Chen
- Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ronghua Wang
- Department of Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Pengtao Xie
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla
| | - Zhenlong Luo
- Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Sideng Cheng
- Department of Computer Science, Algoma University, Sault Ste. Marie, Ontario, Canada
| | - Wujun Wang
- Wuhan EndoAngel Medical Technology Company, Wuhan, China
| | - Xing Li
- Wuhan EndoAngel Medical Technology Company, Wuhan, China
| | - Dingkun Xiong
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xinyuan Cao
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Shuya Bai
- Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Aiming Yang
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Bin Cheng
- Department of Gastroenterology and Hepatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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21
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Bai Y, Qin X, Ao X, Ran T, Zhou C, Zou D. The role of EUS in the diagnosis of early chronic pancreatitis. Endosc Ultrasound 2024; 13:232-238. [PMID: 39318759 PMCID: PMC11419561 DOI: 10.1097/eus.0000000000000077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Accepted: 05/27/2024] [Indexed: 09/26/2024] Open
Abstract
The diagnosis of early chronic pancreatitis (ECP) is challenging due to the lack of standardized diagnostic criteria. EUS has been considered a sensitive diagnostic modality for chronic pancreatitis (CP), with advancements in technique such as EUS-guided fine needle aspiration and biopsy (EUS-FNA/FNB) being developed. However, their role in the diagnosis of ECP remains unelucidated. This review thereby aimed to provide an overview of the clinical landscape of EUS in the field of ECP.
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Affiliation(s)
- Yaya Bai
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xianzheng Qin
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xiang Ao
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Taojing Ran
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Chunhua Zhou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
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Mukund A, Afridi MA, Karolak A, Park MA, Permuth JB, Rasool G. Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence. Cancers (Basel) 2024; 16:2240. [PMID: 38927945 PMCID: PMC11201559 DOI: 10.3390/cancers16122240] [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: 05/07/2024] [Revised: 06/03/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) remains one of the most formidable challenges in oncology, characterized by its late detection and poor prognosis. Artificial intelligence (AI) and machine learning (ML) are emerging as pivotal tools in revolutionizing PDAC care across various dimensions. Consequently, many studies have focused on using AI to improve the standard of PDAC care. This review article attempts to consolidate the literature from the past five years to identify high-impact, novel, and meaningful studies focusing on their transformative potential in PDAC management. Our analysis spans a broad spectrum of applications, including but not limited to patient risk stratification, early detection, and prediction of treatment outcomes, thereby highlighting AI's potential role in enhancing the quality and precision of PDAC care. By categorizing the literature into discrete sections reflective of a patient's journey from screening and diagnosis through treatment and survivorship, this review offers a comprehensive examination of AI-driven methodologies in addressing the multifaceted challenges of PDAC. Each study is summarized by explaining the dataset, ML model, evaluation metrics, and impact the study has on improving PDAC-related outcomes. We also discuss prevailing obstacles and limitations inherent in the application of AI within the PDAC context, offering insightful perspectives on potential future directions and innovations.
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Affiliation(s)
- Ashwin Mukund
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
| | - Muhammad Ali Afridi
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan;
| | - Aleksandra Karolak
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
| | - Margaret A. Park
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (M.A.P.); (J.B.P.)
| | - Jennifer B. Permuth
- Departments of Cancer Epidemiology and Gastrointestinal Oncology, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (M.A.P.); (J.B.P.)
| | - Ghulam Rasool
- Department of Machine Learning, Moffitt Cancer Center and Research Institute, 12902 USF Magnolia Drive, Tampa, FL 33612, USA; (A.M.); (A.K.)
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Rousta F, Esteki A, Shalbaf A, Sadeghi A, Moghadam PK, Voshagh A. Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108205. [PMID: 38703435 DOI: 10.1016/j.cmpb.2024.108205] [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: 02/04/2024] [Revised: 04/13/2024] [Accepted: 04/24/2024] [Indexed: 05/06/2024]
Abstract
The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator. AI performs a critical function in the detection, classification, and segmentation of medical images. The utilization of AI-assisted systems has improved the accuracy and productivity of pancreatic analysis, including the detection of diverse pancreatic disorders (e.g., pancreatitis, masses, and cysts) as well as landmarks and parenchyma. This systematic review examines the rapidly developing domain of AI-assisted system in EUS of the pancreas. Its objective is to present a thorough study of the present research status and developments in this area. This paper explores the significant challenges of AI-assisted system in pancreas EUS imaging, highlights the potential of AI techniques in addressing these challenges, and suggests the scope for future research in domain of AI-assisted EUS systems.
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Affiliation(s)
- Fatemeh Rousta
- Department of Biomedical Engineering and Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Esteki
- Department of Biomedical Engineering and Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Amir Sadeghi
- Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Pardis Ketabi Moghadam
- Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ardalan Voshagh
- Faculty of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
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24
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Rey JF. As how artificial intelligence is revolutionizing endoscopy. Clin Endosc 2024; 57:302-308. [PMID: 38454543 PMCID: PMC11133999 DOI: 10.5946/ce.2023.230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/11/2023] [Accepted: 10/15/2023] [Indexed: 03/09/2024] Open
Abstract
With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.
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Affiliation(s)
- Jean-Francois Rey
- Institut Arnaut Tzanck Gastrointestinal Unt, Saint Laurent du Var, France
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25
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Takahashi H, Ohno E, Furukawa T, Yamao K, Ishikawa T, Mizutani Y, Iida T, Shiratori Y, Oyama S, Koyama J, Mori K, Hayashi Y, Oda M, Suzuki T, Kawashima H. Artificial intelligence in a prediction model for postendoscopic retrograde cholangiopancreatography pancreatitis. Dig Endosc 2024; 36:463-472. [PMID: 37448120 DOI: 10.1111/den.14622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
OBJECTIVES In this study we aimed to develop an artificial intelligence-based model for predicting postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP). METHODS We retrospectively reviewed ERCP patients at Nagoya University Hospital (NUH) and Toyota Memorial Hospital (TMH). We constructed two prediction models, a random forest (RF), one of the machine-learning algorithms, and a logistic regression (LR) model. First, we selected features of each model from 40 possible features. Then the models were trained and validated using three fold cross-validation in the NUH cohort and tested in the TMH cohort. The area under the receiver operating characteristic curve (AUROC) was used to assess model performance. Finally, using the output parameters of the RF model, we classified the patients into low-, medium-, and high-risk groups. RESULTS A total of 615 patients at NUH and 544 patients at TMH were enrolled. Ten features were selected for the RF model, including albumin, creatinine, biliary tract cancer, pancreatic cancer, bile duct stone, total procedure time, pancreatic duct injection, pancreatic guidewire-assisted technique without a pancreatic stent, intraductal ultrasonography, and bile duct biopsy. In the three fold cross-validation, the RF model showed better predictive ability than the LR model (AUROC 0.821 vs. 0.660). In the test, the RF model also showed better performance (AUROC 0.770 vs. 0.663, P = 0.002). Based on the RF model, we classified the patients according to the incidence of PEP (2.9%, 10.0%, and 23.9%). CONCLUSION We developed an RF model. Machine-learning algorithms could be powerful tools to develop accurate prediction models.
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Affiliation(s)
- Hidekazu Takahashi
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Eizaburo Ohno
- Department of Gastroenterology and Hepatology, Fujita Health University Graduate School of Medicine, Aichi, Japan
| | - Taiki Furukawa
- Department of Medical IT, Nagoya University Hospital, Aichi, Japan
| | - Kentaro Yamao
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Takuya Ishikawa
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Yasuyuki Mizutani
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Tadashi Iida
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | | | - Shintaro Oyama
- Department of Medical IT, Nagoya University Hospital, Aichi, Japan
| | - Junji Koyama
- Department of Respiratory Medicine, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Kensaku Mori
- Department of Intelligent Systems, Nagoya University Graduate School of Informatics, Aichi, Japan
| | - Yuichiro Hayashi
- Department of Intelligent Systems, Nagoya University Graduate School of Informatics, Aichi, Japan
| | - Masahiro Oda
- Information Strategy Office, Information and Communications, Nagoya University, Aichi, Japan
| | - Takahisa Suzuki
- Department of Gastroenterology, Toyota Memorial Hospital, Aichi, Japan
| | - Hiroki Kawashima
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
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Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Fukui T, Urata M, Yamamoto Y. Current status of artificial intelligence analysis for the treatment of pancreaticobiliary diseases using endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. DEN OPEN 2024; 4:e267. [PMID: 37397344 PMCID: PMC10312781 DOI: 10.1002/deo2.267] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/18/2023] [Indexed: 07/04/2023]
Abstract
Pancreatic and biliary diseases encompass a range of conditions requiring accurate diagnosis for appropriate treatment strategies. This diagnosis relies heavily on imaging techniques like endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography. Artificial intelligence (AI), including machine learning and deep learning, is becoming integral in medical imaging and diagnostics, such as the detection of colorectal polyps. AI shows great potential in diagnosing pancreatobiliary diseases. Unlike machine learning, which requires feature extraction and selection, deep learning can utilize images directly as input. Accurate evaluation of AI performance is a complex task due to varied terminologies, evaluation methods, and development stages. Essential aspects of AI evaluation involve defining the AI's purpose, choosing appropriate gold standards, deciding on the validation phase, and selecting reliable validation methods. AI, particularly deep learning, is increasingly employed in endoscopic ultrasonography and endoscopic retrograde cholangiopancreatography diagnostics, achieving high accuracy levels in detecting and classifying various pancreatobiliary diseases. The AI often performs better than doctors, even in tasks like differentiating benign from malignant pancreatic tumors, cysts, and subepithelial lesions, identifying gallbladder lesions, assessing endoscopic retrograde cholangiopancreatography difficulty, and evaluating the biliary strictures. The potential for AI in diagnosing pancreatobiliary diseases, especially where other modalities have limitations, is considerable. However, a crucial constraint is the need for extensive, high-quality annotated data for AI training. Future advances in AI, such as large language models, promise further applications in the medical field.
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Affiliation(s)
| | - Kazuo Hara
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nobumasa Mizuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Shin Haba
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Nozomi Okuno
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Toshitaka Fukui
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
| | - Minako Urata
- Department of GastroenterologyAichi Cancer Center HospitalAichiJapan
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Mascarenhas Saraiva M, Spindler L, Fathallah N, Beaussier H, Mamma C, Ribeiro T, Afonso J, Carvalho M, Moura R, Cardoso P, Mendes F, Martins M, Adam J, Ferreira J, Macedo G, de Parades V. Deep Learning in High-Resolution Anoscopy: Assessing the Impact of Staining and Therapeutic Manipulation on Automated Detection of Anal Cancer Precursors. Clin Transl Gastroenterol 2024; 15:e00681. [PMID: 38270249 DOI: 10.14309/ctg.0000000000000681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION High-resolution anoscopy (HRA) is the gold standard for detecting anal squamous cell carcinoma (ASCC) precursors. Preliminary studies on the application of artificial intelligence (AI) models to this modality have revealed promising results. However, the impact of staining techniques and anal manipulation on the effectiveness of these algorithms has not been evaluated. We aimed to develop a deep learning system for automatic differentiation of high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion in HRA images in different subsets of patients (nonstained, acetic acid, lugol, and after manipulation). METHODS A convolutional neural network was developed to detect and differentiate high-grade and low-grade anal squamous intraepithelial lesions based on 27,770 images from 103 HRA examinations performed in 88 patients. Subanalyses were performed to evaluate the algorithm's performance in subsets of images without staining, acetic acid, lugol, and after manipulation of the anal canal. The sensitivity, specificity, accuracy, positive and negative predictive values, and area under the curve were calculated. RESULTS The convolutional neural network achieved an overall accuracy of 98.3%. The algorithm had a sensitivity and specificity of 97.4% and 99.2%, respectively. The accuracy of the algorithm for differentiating high-grade squamous intraepithelial lesion vs low-grade squamous intraepithelial lesion varied between 91.5% (postmanipulation) and 100% (lugol) for the categories at subanalysis. The area under the curve ranged between 0.95 and 1.00. DISCUSSION The introduction of AI to HRA may provide an accurate detection and differentiation of ASCC precursors. Our algorithm showed excellent performance at different staining settings. This is extremely important because real-time AI models during HRA examinations can help guide local treatment or detect relapsing disease.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, Porto, Portugal
| | - Lucas Spindler
- Department of Proctology, GH Paris Saint-Joseph, Paris, France
| | - Nadia Fathallah
- Department of Proctology, GH Paris Saint-Joseph, Paris, France
| | - Hélene Beaussier
- Department of Clinical Research, GH Paris Saint-Joseph, Paris, France
| | - Célia Mamma
- Department of Clinical Research, GH Paris Saint-Joseph, Paris, France
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Mariana Carvalho
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Rita Moura
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Pedro Cardoso
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Francisco Mendes
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Miguel Martins
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - Julien Adam
- Department of Pathology, GH Paris Saint-Joseph, Paris, France
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Alameda Professor Hernâni Monteiro, Porto, Portugal
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Zhao SQ, Liu WT. Progress in artificial intelligence assisted digestive endoscopy diagnosis of digestive system diseases. WORLD CHINESE JOURNAL OF DIGESTOLOGY 2024; 32:171-181. [DOI: 10.11569/wcjd.v32.i3.171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/29/2024]
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29
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Xu YC, Fu DL, Yang F. Unraveling the enigma: A comprehensive review of solid pseudopapillary tumor of the pancreas. World J Gastrointest Oncol 2024; 16:614-629. [PMID: 38577449 PMCID: PMC10989376 DOI: 10.4251/wjgo.v16.i3.614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/28/2023] [Accepted: 01/16/2024] [Indexed: 03/12/2024] Open
Abstract
Solid pseudopapillary tumor of the pancreas (SPTP) is a rare neoplasm predominantly observed in young females. Pathologically, CTNNB1 mutations, β-catenin nuclear accumulation, and subsequent Wnt-signaling pathway activation are the leading molecular features. Accurate preoperative diagnosis often relies on imaging techniques and endoscopic biopsies. Surgical resection remains the mainstay treatment. Risk models, such as the Fudan Prognostic Index, show promise as predictive tools for assessing the prognosis of SPTP. Establishing three types of metachronous liver metastasis can be beneficial in tailoring individualized treatment and follow-up strategies. Despite advancements, challenges persist in understanding its etiology, establishing standardized treatments for unresectable or metastatic diseases, and developing a widely recognized grading system. This comprehensive review aims to elucidate the enigma by consolidating current knowledge on the epidemiology, clinical presentation, pathology, molecular characteristics, diagnostic methods, treatment options, and prognostic factors.
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Affiliation(s)
- Ye-Cheng Xu
- Department of Pancreatic Surgery, Pancreatic Disease Institute, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - De-Liang Fu
- Department of Pancreatic Surgery, Pancreatic Disease Institute, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Feng Yang
- Department of Pancreatic Surgery, Pancreatic Disease Institute, Huashan Hospital, Fudan University, Shanghai 200040, China
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Juneja D. Artificial intelligence: Applications in critical care gastroenterology. Artif Intell Gastrointest Endosc 2024; 5:89138. [DOI: 10.37126/aige.v5.i1.89138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/07/2023] [Accepted: 12/26/2023] [Indexed: 02/20/2024] Open
Abstract
Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.
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Affiliation(s)
- Deven Juneja
- Department of Critical Care Medicine, Max Super Speciality Hospital, New Delhi 110017, India
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Abstract
Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.
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Affiliation(s)
- Deyu Zhang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Chang Wu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhenghui Yang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China
| | - Yue Liu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Wanshun Li
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Haojie Huang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhendong Jin
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
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Dhali A, Kipkorir V, Srichawla BS, Kumar H, Rathna RB, Ongidi I, Chaudhry T, Morara G, Nurani K, Cheruto D, Biswas J, Chieng LR, Dhali GK. Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space-occupying lesion: a systematic review and meta-analysis. Int J Surg 2023; 109:4298-4308. [PMID: 37800594 PMCID: PMC10720860 DOI: 10.1097/js9.0000000000000717] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 08/21/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Diagnosing pancreatic lesions, including chronic pancreatitis, autoimmune pancreatitis, and pancreatic cancer, poses a challenge and, as a result, is time-consuming. To tackle this issue, artificial intelligence (AI) has been increasingly utilized over the years. AI can analyze large data sets with heightened accuracy, reduce interobserver variability, and can standardize the interpretation of radiologic and histopathologic lesions. Therefore, this study aims to review the use of AI in the detection and differentiation of pancreatic space-occupying lesions and to compare AI-assisted endoscopic ultrasound (EUS) with conventional EUS in terms of their detection capabilities. METHODS Literature searches were conducted through PubMed/Medline, SCOPUS, and Embase to identify studies eligible for inclusion. Original articles, including observational studies, randomized control trials, systematic reviews, meta-analyses, and case series specifically focused on AI-assisted EUS in adults, were included. Data were extracted and pooled, and a meta-analysis was conducted using Meta-xl. For results exhibiting significant heterogeneity, a random-effects model was employed; otherwise, a fixed-effects model was utilized. RESULTS A total of 21 studies were included in the review with four studies pooled for a meta-analysis. A pooled accuracy of 93.6% (CI 90.4-96.8%) was found using the random-effects model on four studies that showed significant heterogeneity ( P <0.05) in the Cochrane's Q test. Further, a pooled sensitivity of 93.9% (CI 92.4-95.3%) was found using a fixed-effects model on seven studies that showed no significant heterogeneity in the Cochrane's Q test. When it came to pooled specificity, a fixed-effects model was utilized in six studies that showed no significant heterogeneity in the Cochrane's Q test and determined as 93.1% (CI 90.7-95.4%). The pooled positive predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 91.6% (CI 87.3-95.8%). The pooled negative predictive value which was done using the random-effects model on six studies that showed significant heterogeneity was 93.6% (CI 90.4-96.8%). CONCLUSION AI-assisted EUS shows a high degree of accuracy in the detection and differentiation of pancreatic space-occupying lesions over conventional EUS. Its application may promote prompt and accurate diagnosis of pancreatic pathologies.
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Affiliation(s)
- Arkadeep Dhali
- NIHR Academic Clinical Fellow in Gastroenterology, University of Sheffield; Internal Medicine Trainee, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Vincent Kipkorir
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | | | | | | | - Ibsen Ongidi
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Talha Chaudhry
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Gisore Morara
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Khulud Nurani
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | - Doreen Cheruto
- School of Medicine, Faculty of Health Sciences, University of Nairobi, Nairobi, Kenya
| | | | - Leonard R. Chieng
- NIHR Academic Clinical Fellow in Gastroenterology, University of Sheffield; Internal Medicine Trainee, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Gopal Krishna Dhali
- School of Digestive and Liver Diseases, Institute of Postgraduate Medical Education and Research, Kolkata, India
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Vitali F, Zundler S, Jesper D, Wildner D, Strobel D, Frulloni L, Neurath MF. Diagnostic Endoscopic Ultrasound in Pancreatology: Focus on Normal Variants and Pancreatic Masses. Visc Med 2023; 39:121-130. [PMID: 37899794 PMCID: PMC10601528 DOI: 10.1159/000533432] [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: 05/19/2023] [Accepted: 08/03/2023] [Indexed: 10/31/2023] Open
Abstract
Background Endoscopic ultrasound (EUS) is a main tool in gastroenterology for both diagnosis and exclusion of pancreatic pathology. It allows minimally invasive assessment of various diseases or anatomic variations affecting the pancreas also with the help of new Doppler technologies, elastography, contrast-enhanced imaging including post hoc image processing with quantification analyses, three-dimensional reconstruction, and artificial intelligence. EUS also allows interventional direct access to the pancreatic parenchyma and the retroperitoneal space, to the pancreatic duct, pancreatic masses, cysts, and vascular structures. Summary This review aimed to summarize new developments of EUS in the field of pancreatology. We highlight the role of EUS in evaluating pancreatic pathology by describing normal anatomic variants like pancreas divisum, pancreatic lipomatosis, pancreatic fibrosis in the elderly and characterizing pancreatic masses, both in the context of chronic pancreatitis and within healthy pancreatic parenchyma. EUS is considered the optimal imaging modality for pancreatic masses of uncertain dignity and allows both cytological diagnosis and histology, which is essential not only for neoplastic conditions but also for tailoring therapy for benign inflammatory conditions. Key Messages EUS plays an indispensable role in pancreatology and the development of new diagnostic and interventional approaches to the retroperitoneal space and the pancreas exponentially increased over the last years. The development of computer-aided diagnosis and artificial intelligence algorithms hold the potential to overcome the obstacles associated with interobserver variability and will most likely support decision-making in the management of pancreatic disease.
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Affiliation(s)
- Francesco Vitali
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Sebastian Zundler
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Daniel Jesper
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Dane Wildner
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Deike Strobel
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
| | - Luca Frulloni
- Department of Medicine, Gastroenterology Unit, Pancreas Center, University of Verona, Verona, Italy
| | - Markus F. Neurath
- Department of Medicine 1, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
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Huang J, Fan X, Liu W. Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases. Diagnostics (Basel) 2023; 13:2815. [PMID: 37685350 PMCID: PMC10487217 DOI: 10.3390/diagnostics13172815] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/22/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023] Open
Abstract
Endoscopic ultrasound (EUS) has emerged as a widely utilized tool in the diagnosis of digestive diseases. In recent years, the potential of artificial intelligence (AI) in healthcare has been gradually recognized, and its superiority in the field of EUS is becoming apparent. Machine learning (ML) and deep learning (DL) are the two main AI algorithms. This paper aims to outline the applications and prospects of artificial intelligence-assisted endoscopic ultrasound (EUS-AI) in digestive diseases over the past decade. The results demonstrated that EUS-AI has shown superiority or at least equivalence to traditional methods in the diagnosis, prognosis, and quality control of subepithelial lesions, early esophageal cancer, early gastric cancer, and pancreatic diseases including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer. The implementation of EUS-AI has opened up new avenues for individualized precision medicine and has introduced novel diagnostic and treatment approaches for digestive diseases.
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Affiliation(s)
| | | | - Wentian Liu
- Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China; (J.H.); (X.F.)
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Lv B, Wang K, Wei N, Yu F, Tao T, Shi Y. Diagnostic value of deep learning-assisted endoscopic ultrasound for pancreatic tumors: a systematic review and meta-analysis. Front Oncol 2023; 13:1191008. [PMID: 37576885 PMCID: PMC10414790 DOI: 10.3389/fonc.2023.1191008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 07/13/2023] [Indexed: 08/15/2023] Open
Abstract
Background and aims Endoscopic ultrasonography (EUS) is commonly utilized in the diagnosis of pancreatic tumors, although as this modality relies primarily on the practitioner's visual judgment, it is prone to result in a missed diagnosis or misdiagnosis due to inexperience, fatigue, or distraction. Deep learning (DL) techniques, which can be used to automatically extract detailed imaging features from images, have been increasingly beneficial in the field of medical image-based assisted diagnosis. The present systematic review included a meta-analysis aimed at evaluating the accuracy of DL-assisted EUS for the diagnosis of pancreatic tumors diagnosis. Methods We performed a comprehensive search for all studies relevant to EUS and DL in the following four databases, from their inception through February 2023: PubMed, Embase, Web of Science, and the Cochrane Library. Target studies were strictly screened based on specific inclusion and exclusion criteria, after which we performed a meta-analysis using Stata 16.0 to assess the diagnostic ability of DL and compare it with that of EUS practitioners. Any sources of heterogeneity were explored using subgroup and meta-regression analyses. Results A total of 10 studies, involving 3,529 patients and 34,773 training images, were included in the present meta-analysis. The pooled sensitivity was 93% (95% confidence interval [CI], 87-96%), the pooled specificity was 95% (95% CI, 89-98%), and the area under the summary receiver operating characteristic curve (AUC) was 0.98 (95% CI, 0.96-0.99). Conclusion DL-assisted EUS has a high accuracy and clinical applicability for diagnosing pancreatic tumors. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023391853, identifier CRD42023391853.
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Affiliation(s)
- Bing Lv
- School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong, China
| | - Kunhong Wang
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Ning Wei
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Feng Yu
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Tao Tao
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
| | - Yanting Shi
- Department of Gastroenterology, Zibo Central Hospital, Zibo, Shandong, China
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Rey JF. Artificial intelligence in digestive endoscopy: recent advances. Curr Opin Gastroenterol 2023:00001574-990000000-00089. [PMID: 37522929 DOI: 10.1097/mog.0000000000000957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
PURPOSE OF REVIEW With the incessant advances in information technology and its implications in all domains of our life, artificial intelligence (AI) started to emerge as a need for better machine performance. How it can help endoscopists and what are the areas of interest in improving both diagnostic and therapeutic endoscopy in each part of the gastrointestinal (GI) tract. What are the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. RECENT FINDINGS The two main AI systems categories are computer-assisted detection 'CADe' for lesion detection and computer-assisted diagnosis 'CADx' for optical biopsy and lesion characterization. Multiple softwares are now implemented in endoscopy practice. Other AI systems offer therapeutic assistance such as lesion delineation for complete endoscopic resection or prediction of possible lymphanode after endoscopic treatment. Quality assurance is the coming step with complete monitoring of high-quality colonoscopy. In all cases it is a computer-aid endoscopy as the overall result rely on the physician. Video capsule endoscopy is the unique example were the computer conduct the device, store multiple images, and perform accurate diagnosis. SUMMARY AI is a breakthrough in digestive endoscopy. Screening gastric and colonic cancer detection should be improved especially outside of expert's centers. Prospective and multicenter trials are mandatory before introducing new software in clinical practice.
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Affiliation(s)
- Jean-Francois Rey
- Arnault Tzanck Institute, 116 rue du commandant Cahuzac, Saint Laurent du var, France
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Zhao Q, Jia Q, Chi T. Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case-control study. BMC Gastroenterol 2022; 22:352. [PMID: 35879649 PMCID: PMC9310473 DOI: 10.1186/s12876-022-02427-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 07/15/2022] [Indexed: 11/28/2022] Open
Abstract
Background and aims Chronic atrophic gastritis (CAG) is a precancerous disease that often leads to the development of gastric cancer (GC) and is positively correlated with GC morbidity. However, the sensitivity of the endoscopic diagnosis of CAG is only 42%. Therefore, we developed a real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net deep learning (DL) and conducted a prospective nested case–control study to evaluate the diagnostic evaluation indices of the model and its consistency with pathological diagnosis.
Methods Our cohort consisted of 1539 patients undergoing gastroscopy from December 1, 2020, to July 1, 2021. Based on pathological diagnosis, patients in the cohort were divided into the CAG group or the chronic nonatrophic gastritis (CNAG) group, and we assessed the diagnostic evaluation indices of this model and its consistency with pathological diagnosis after propensity score matching (PSM) to minimize selection bias in the study. Results After matching, the diagnostic evaluation indices and consistency evaluation of the model were better than those of endoscopists [sensitivity (84.02% vs. 62.72%), specificity (97.04% vs. 81.95%), positive predictive value (96.60% vs. 77.66%), negative predictive value (85.86% vs. 68.73%), accuracy rate (90.53% vs. 72.34%), Youden index (81.06% vs. 44.67%), odd product (172.5 vs. 7.64), positive likelihood ratio (28.39 vs. 3.47), negative likelihood ratio (0.16 vs. 0.45), AUC (95% CI) [0.909 (0.884–0.934) vs. 0.740 (0.702–0.778)] and Kappa (0.852 vs. 0.558)]. Conclusions Our prospective nested case–control study proved that the diagnostic evaluation indices and consistency evaluation of the real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net DL were superior to those of endoscopists. Trial registrationChiCTR2100044458, 18/03/2020.
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Affiliation(s)
- Quchuan Zhao
- Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, 45 Chang-chun Street, Beijing, 100053, China
| | - Qing Jia
- Department of Anesthesiology, Guang'anmen Hospital China Academy of Chinese Medical Sciences, 5 North Court Street, Beijing, 100053, China.
| | - Tianyu Chi
- Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, 45 Chang-chun Street, Beijing, 100053, China.
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