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Holt NM, Byrne MF. The Role of Artificial Intelligence and Big Data for Gastrointestinal Disease. Gastrointest Endosc Clin N Am 2025; 35:291-308. [PMID: 40021230 DOI: 10.1016/j.giec.2024.09.004] [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) is a rapidly evolving presence in all fields and industries, with the ability to both improve quality and reduce the burden of human effort. Gastroenterology is a field with a focus on diagnostic techniques and procedures, and AI and big data have established and growing roles to play. Alongside these opportunities are challenges, which will evolve in parallel.
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Affiliation(s)
- Nicholas Mathew Holt
- Gastroenterology and Hepatology Unit, The Canberra Hospital, Yamba Drive, Garran, ACT 2605, Australia.
| | - Michael Francis Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, UBC Division of Gastroenterology, 5153 - 2775 Laurel Street, Vancouver, British Columbia V5Z 1M9, Canada
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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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Chiang SY, Wang YW, Su PY, Chang YY, Yen HH, Chang RF. PBCS-ConvNeXt: Convolutional Network-Based Automatic Diagnosis of Non-alcoholic Fatty Liver in Abdominal Ultrasound Images. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025:10.1007/s10278-025-01394-w. [PMID: 39841370 DOI: 10.1007/s10278-025-01394-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Revised: 11/25/2024] [Accepted: 12/24/2024] [Indexed: 01/23/2025]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a highly prevalent chronic liver condition characterized by excessive hepatic fat accumulation. Early diagnosis is crucial as NAFLD can progress to more severe conditions like steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma without timely intervention. While liver biopsy remains the gold standard for NAFLD assessment, abdominal ultrasound (US) imaging has emerged as a widely adopted non-invasive modality due to convenience and low cost. However, the subjective interpretation of US images is challenging and unpredictable. This study proposes a deep learning-based computer-aided diagnosis (CAD) model, termed potent boosts channel-aware separable intent - ConvNeXt (PBCS-ConvNeXt), for automated NAFLD classification using B-mode US images. The model architecture comprises three key components: The potent stem cell, an advanced trainable preprocessing module for robust feature extraction; Enhanced ConvNeXt Blocks that amplify channel-wise features to refine processing; and the boosting block that integrates multi-stage features for effective information extraction from US data. Utilizing fatty liver gradings from attenuation imaging (ATI) as the ground truth, the PBCS-ConvNeXt model was evaluated using 5-fold cross-validation, achieving an accuracy of 82%, sensitivity of 81% and specificity of 83% for identifying fatty liver on abdominal US. The proposed CAD system demonstrates high diagnostic performance in NAFLD classification from US images, enabling early detection and informing timely clinical management to prevent disease progression.
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Affiliation(s)
- Shang-Yu Chiang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - You-Wei Wang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Pei-Yuan Su
- Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Yuan-Yen Chang
- Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung, Taiwan
| | - Hsu-Heng Yen
- Division of Gastroenterology, Changhua Christian Hospital, Changhua, Taiwan.
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.
- Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua, Taiwan.
| | - Ruey-Feng Chang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan.
- Graduate Institute of Network and Multimedia, National Taiwan University, Taipei, Taiwan.
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Mahajan S, Siyu S, Bhutani MS. What can artificial intelligence do for EUS? Endosc Ultrasound 2025; 14:1-3. [DOI: 10.1097/eus.0000000000000102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/17/2025] Open
Affiliation(s)
| | - Sun Siyu
- Shengjing hospital of China Medical University, Shenyang, Liaoning Province, China
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Dafni MF, Shih M, Manoel AZ, Yousif MYE, Spathi S, Harshal C, Bhatt G, Chodnekar SY, Chune NS, Rasool W, Umar TP, Moustakas DC, Achkar R, Kumar H, Naz S, Acuña-Chavez LM, Evgenikos K, Gulraiz S, Ali ESM, Elaagib A, Uggh IHP. Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention. Cancer Causes Control 2024:10.1007/s10552-024-01942-9. [PMID: 39672997 DOI: 10.1007/s10552-024-01942-9] [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: 07/07/2024] [Accepted: 11/18/2024] [Indexed: 12/15/2024]
Abstract
Artificial intelligence is rapidly changing our world at an exponential rate and its transformative power has extensively reached important sectors like healthcare. In the fight against cancer, AI proved to be a novel and powerful tool, offering new hope for prevention and early detection. In this review, we will comprehensively explore the medical applications of AI, including early cancer detection through pathological and imaging analysis, risk stratification, patient triage, and the development of personalized prevention approaches. However, despite the successful impact AI has contributed to, we will also discuss the myriad of challenges that we have faced so far toward optimal AI implementation. There are problems when it comes to the best way in which we can use AI systemically. Having the correct data that can be understood easily must remain one of the most significant concerns in all its uses including sharing information. Another challenge that exists is how to interpret AI models because they are too complicated for people to follow through examples used in their developments which may affect trust, especially among medical professionals. Other considerations like data privacy, algorithm bias, and equitable access to AI tools have also arisen. Finally, we will evaluate possible future directions for this promising field that highlight AI's capacity to transform preventative cancer care.
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Affiliation(s)
- Marianna-Foteini Dafni
- School of Medicine, Laboratory of Forensic Medicine and Toxicology, Aristotle Univerisity of Thessaloniki, Thessaloniki, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Mohamed Shih
- School of Medicine, Newgiza University, Giza, Egypt.
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece.
| | - Agnes Zanotto Manoel
- Faculty of Medicine, Federal University of Rio Grande, Rio Grande do Sul, Brazil
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Mohamed Yousif Elamin Yousif
- Faculty of Medicine, University of Khartoum, Khartoum, Sudan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Stavroula Spathi
- Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Chorya Harshal
- Faculty of Medicine, Medical College Baroda, Vadodara, India
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Gaurang Bhatt
- All India Institute of Medical Sciences, Rishikesh, India
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Swarali Yatin Chodnekar
- Faculty of Medicine, Teaching University Geomedi LLC, Tbilisi, Georgia
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Nicholas Stam Chune
- Faculty of Medicine, University of Nairobi, Nairobi, Kenya
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Warda Rasool
- Faculty of Medicine, King Edward Medical University, Lahore, Pakistan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Tungki Pratama Umar
- Division of Surgery and Interventional Science, Faculty of Medical Sciences, University College London, London, UK
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Dimitrios C Moustakas
- Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Robert Achkar
- Faculty of Medicine, Poznan University of Medical Sciences, Poznan, Poland
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Harendra Kumar
- Dow University of Health Sciences, Karachi, Pakistan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Suhaila Naz
- Tbilisi State Medical University, Tbilisi, Georgia
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Luis M Acuña-Chavez
- Facultad de Medicina de la Universidad Nacional de Trujillo, Trujillo, Peru
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Konstantinos Evgenikos
- Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Shaina Gulraiz
- Royal Bournemouth Hospital (University Hospitals Dorset), Bournemouth, UK
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Eslam Salih Musa Ali
- University of Dongola Faculty of Medicine and Health Science, Dongola, Sudan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Amna Elaagib
- Faculty of Medicine AlMughtaribeen University, Khartoum, Sudan
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
| | - Innocent H Peter Uggh
- Kilimanjaro Clinical Research Institute, Kilimanjaro, Tanzania
- Cancer Prevention Research Group in Greece, Kifisias Avenue 44, Marousi, Greece
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Harne PS, Harne V, Wray C, Thosani N. Endoscopic innovations in diagnosis and management of pancreatic cancer: a narrative review and future directions. Therap Adv Gastroenterol 2024; 17:17562848241297434. [PMID: 39664230 PMCID: PMC11632891 DOI: 10.1177/17562848241297434] [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: 05/15/2024] [Accepted: 10/15/2024] [Indexed: 12/13/2024] Open
Abstract
Pancreatic cancer serves as the third leading cause of cancer-associated morbidity and mortality in the United States, with a 5-year survival rate of only 12% with an expected increase in incidence and mortality in the coming years. Pancreatic ductal adenocarcinomas constitute most pancreatic malignancies. Certain genetic syndromes, including Lynch syndrome, hereditary breast and ovarian cancer syndrome, hereditary pancreatitis, familial adenomatous polyposis, Peutz-Jeghers syndrome, familial pancreatic cancer mutation, and ataxia telangiectasia, confer a significantly higher risk. Screening for pancreatic malignancies currently targets patients with germline mutations or those with significant family history. Screening the general population is not currently viable owing to overall low incidence and lack of specific tests. Endoscopic ultrasound (EUS) and its applied advances are increasingly being used for surveillance, diagnosis, and management of pancreatic malignancies and have now become an indispensable tool in their management. For patients with risk factors, EUS in combination with magnetic resonance imaging/magnetic resonance cholangiopancreatography is used for screening. The role of endoscopic modalities has been expanding with the increased utilization of endoscopic retrograde cholangiopancreatography, EUS-directed therapies include EUS-guided fine-needle aspiration and EUS-fine-needle biopsy (FNB). EUS combined with FNB has the highest specificity and sensitivity for detecting pancreatic cancer amongst available modalities. Studies also recognize that artificial intelligence assisted EUS in the early detection of pancreatic cancer. At the same time, surgical resection has been historically considered the only curative treatment for pancreatic cancer, over 80% of patients present with unresectable disease. We also discuss EUS-guided therapies of physicochemicals (radiofrequency ablation, brachytherapy, and intratumor chemotherapy), biological agents (gene therapies and oncolytic viruses), and immunotherapy. We aim to perform a detailed review of the current burden, risk factors, role of screening, diagnosis, and endoscopic advances in the treatment modalities available for pancreatic cancer.
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Affiliation(s)
- Prateek Suresh Harne
- Division of Gastroenterology, Allegheny Health Network, Pittsburgh, PA 15212, USA
| | - Vaishali Harne
- Division of Pediatric Gastroenterology, The University of Texas
- Health Science Center and McGovern School of Medicine, Houston, TX, USA
| | - Curtis Wray
- Department of Surgery, The University of Texas Health Science Center and McGovern School of Medicine, Houston, TX, USA
| | - Nirav Thosani
- Department of Surgery and Interventional Gastroenterology, The University of Texas
- Health Science Center and McGovern School of Medicine, Houston, TX, USA
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Orzan RI, Santa D, Lorenzovici N, Zareczky TA, Pojoga C, Agoston R, Dulf EH, Seicean A. Deep Learning in Endoscopic Ultrasound: A Breakthrough in Detecting Distal Cholangiocarcinoma. Cancers (Basel) 2024; 16:3792. [PMID: 39594747 PMCID: PMC11593152 DOI: 10.3390/cancers16223792] [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: 10/03/2024] [Revised: 10/30/2024] [Accepted: 11/08/2024] [Indexed: 11/28/2024] Open
Abstract
INTRODUCTION Cholangiocarcinoma (CCA) is a highly lethal malignancy originating in the bile ducts, often diagnosed late with poor prognosis. Differentiating benign from malignant biliary tumors remains challenging, necessitating advanced diagnostic techniques. OBJECTIVE This study aims to enhance the diagnostic accuracy of endoscopic ultrasound (EUS) for distal cholangiocarcinoma (dCCA) using advanced convolutional neural networks (CCNs) for the classification and segmentation of EUS images, specifically targeting dCCAs, the pancreas, and the bile duct. MATERIALS AND METHODS In this retrospective study, EUS images from patients diagnosed with dCCA via biopsy and an EUS-identified bile duct tumor were evaluated. A custom CNN was developed for classification, trained on 156 EUS images. To enhance the model's robustness, image augmentation techniques were applied, generating a total of 1248 images. For tumor and organ segmentation, the DeepLabv3+ network with ResNet50 architecture was utilized, employing Tversky loss to manage unbalanced classes. Performance evaluation included metrics such as accuracy, sensitivity, specificity, and Intersection over Union (IoU). These methods were implemented in collaboration with the ADAPTED Research Group at the Technical University of Cluj-Napoca. RESULTS The classification model achieved a high accuracy of 97.82%, with precision and specificity both at 100% and sensitivity at 94.44%. The segmentation models for the pancreas and bile duct demonstrated global accuracies of 84% and 90%, respectively, with robust IoU scores indicating good overlap between predicted and actual contours. The application performed better than the UNet model, particularly in generalization and boundary delineation. CONCLUSIONS This study demonstrates the significant potential of AI in EUS imaging for dCCA, presenting a robust tool that enhances diagnostic accuracy and efficiency. The developed MATLAB application serves as a valuable aid for medical professionals, facilitating informed decision-making and improving patient outcomes in the diagnosis of cholangiocarcinoma and related pathologies.
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Affiliation(s)
- Rares Ilie Orzan
- 3rd Department of Internal Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, Victor Babeș Str., No. 8, 400012 Cluj-Napoca, Romania
- Regional Institute of Gastroenterology and Hepatology, Croitorilor Str., No. 19-21, 400162 Cluj-Napoca, Romania;
| | - Delia Santa
- Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, G. Baritiu Str., No. 26-28, 400027 Cluj-Napoca, Romania (N.L.)
| | - Noemi Lorenzovici
- Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, G. Baritiu Str., No. 26-28, 400027 Cluj-Napoca, Romania (N.L.)
| | - Thomas Andrei Zareczky
- Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, G. Baritiu Str., No. 26-28, 400027 Cluj-Napoca, Romania (N.L.)
| | - Cristina Pojoga
- Regional Institute of Gastroenterology and Hepatology, Croitorilor Str., No. 19-21, 400162 Cluj-Napoca, Romania;
- Department of Clinical Psychology and Psychotherapy, Babeș-Bolyai University, Sindicatelor Str., No. 7, 400029 Cluj-Napoca, Romania
| | - Renata Agoston
- Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, Victor Babes Str., No. 8, 400012 Cluj-Napoca, Romania
| | - Eva-Henrietta Dulf
- Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, G. Baritiu Str., No. 26-28, 400027 Cluj-Napoca, Romania (N.L.)
| | - Andrada Seicean
- 3rd Department of Internal Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, Victor Babeș Str., No. 8, 400012 Cluj-Napoca, Romania
- Regional Institute of Gastroenterology and Hepatology, Croitorilor Str., No. 19-21, 400162 Cluj-Napoca, Romania;
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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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Bou Jaoude J, Al Bacha R, Abboud B. Will artificial intelligence reach any limit in gastroenterology? Artif Intell Gastroenterol 2024; 5:91336. [DOI: 10.35712/aig.v5.i2.91336] [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: 12/27/2023] [Revised: 04/25/2024] [Accepted: 06/07/2024] [Indexed: 08/08/2024] Open
Abstract
Endoscopy is the cornerstone in the management of digestive diseases. Over the last few decades, technology has played an important role in the development of this field, helping endoscopists in better detecting and characterizing luminal lesions. However, despite ongoing advancements in endoscopic technology, the incidence of missed pre-neoplastic and neoplastic lesions remains high due to the operator-dependent nature of endoscopy and the challenging learning curve associated with new technologies. Artificial intelligence (AI), an operator-independent field, could be an invaluable solution. AI can serve as a “second observer”, enhancing the performance of endoscopists in detecting and characterizing luminal lesions. By utilizing deep learning (DL), an innovation within machine learning, AI automatically extracts input features from targeted endoscopic images. DL encompasses both computer-aided detection and computer-aided diagnosis, assisting endoscopists in reducing missed detection rates and predicting the histology of luminal digestive lesions. AI applications in clinical gastrointestinal diseases are continuously expanding and evolving the entire digestive tract. In all published studies, real-time AI assists endoscopists in improving the performance of non-expert gastroenterologists, bringing it to a level comparable to that of experts. The development of DL may be affected by selection biases. Studies have utilized different AI-assisted models, which are heterogeneous. In the future, algorithms need validation through large, randomized trials. Theoretically, AI has no limit to assist endoscopists in increasing the accuracy and the quality of endoscopic exams. However, practically, we still have a long way to go before standardizing our AI models to be accepted and applied by all gastroenterologists.
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Affiliation(s)
- Joseph Bou Jaoude
- Department of Gastroenterology, Levant Hospital, Beirut 166830, Lebanon
| | - Rose Al Bacha
- Department of Gastroenterology, Levant Hospital, Beirut 166830, Lebanon
| | - Bassam Abboud
- Department of General Surgery, Geitaoui Hospital, Faculty of Medicine, Lebanese University, Lebanon, Beirut 166830, Lebanon
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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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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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12
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Coban S, Zahid KS, Brugge WR. The future of EUS. ENDOSCOPIC ULTRASONOGRAPHY 2024:287-293. [DOI: 10.1002/9781119697893.ch31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2025]
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13
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Scherübl H. [Early detection of sporadic pancreatic cancer]. ZEITSCHRIFT FUR GASTROENTEROLOGIE 2024; 62:412-419. [PMID: 37827502 DOI: 10.1055/a-2114-9847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
The incidence of pancreatic cancer is rising. At present, pancreatic cancer is the third most common cancer-causing death in Germany, but it is expected to become the second in 2030 and finally the leading cause of cancer death in 2050. Pancreatic ductal adenocarcinoma (PC) is generally diagnosed at advanced stages, and 5-year-survival has remained poor. Early detection of sporadic PC at stage IA, however, can yield a 5-year-survival rate of about 80%. Early detection initiatives aim at identifying persons at high risk. People with new-onset diabetes at age 50 or older have attracted much interest. Novel strategies regarding how to detect sporadic PC at an early stage are being discussed.
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Affiliation(s)
- Hans Scherübl
- Klinik für Innere Medizin; Gastroenterol., GI Onkol. u. Infektiol., Vivantes Klinikum Am Urban, Berlin, Germany
- Akademisches Lehrkrankenhaus der Charité, Berlin, Germany
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14
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Chatterjee A, Shah J. Role of Endoscopic Ultrasound in Diagnosis of Pancreatic Ductal Adenocarcinoma. Diagnostics (Basel) 2023; 14:78. [PMID: 38201387 PMCID: PMC10802852 DOI: 10.3390/diagnostics14010078] [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: 12/04/2023] [Revised: 12/26/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the most common (90%) type of solid pancreatic neoplasm. Due to its late presentation and poor survival rate, early diagnosis and timely treatment is of utmost importance for better clinical outcomes. Endoscopic ultrasound provides high-resolution images of the pancreas and has excellent sensitivity in the diagnosis of even small (<2 cm) pancreatic lesions. Apart from imaging, it also has an advantage of tissue acquisition (EUS fine-needle aspiration, FNA; or fine-needle biopsy, FNB) for definitive diagnoses. EUS-guided tissue acquisition plays a crucial role in genomic and molecular studies, which in today's era of personalized medicine, are likely to become important components of PDAC management. With the use of better needle designs and technical advancements, EUS has now become an indispensable tool in the management of PDAC. Lastly, artificial intelligence for the detection of pancreatic lesions and newer automated needles for tissue acquisition will obviate observer dependency in the near future, resulting in the wider dissemination and adoption of this technology for improved outcomes in patients with PDAC.
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Affiliation(s)
| | - Jimil Shah
- Department of Gastroenterology, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh 160012, India;
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15
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Arif AA, Jiang SX, Byrne MF. Artificial intelligence in endoscopy: Overview, applications, and future directions. Saudi J Gastroenterol 2023; 29:269-277. [PMID: 37787347 PMCID: PMC10644999 DOI: 10.4103/sjg.sjg_286_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 08/16/2023] [Indexed: 09/15/2023] Open
Abstract
Since the emergence of artificial intelligence (AI) in medicine, endoscopy applications in gastroenterology have been at the forefront of innovations. The ever-increasing number of studies necessitates the need to organize and classify applications in a useful way. Separating AI capabilities by computer aided detection (CADe), diagnosis (CADx), and quality assessment (CADq) allows for a systematic evaluation of each application. CADe studies have shown promise in accurate detection of esophageal, gastric and colonic neoplasia as well as identifying sources of bleeding and Crohn's disease in the small bowel. While more advanced CADx applications employ optical biopsies to give further information to characterize neoplasia and grade inflammatory disease, diverse CADq applications ensure quality and increase the efficiency of procedures. Future applications show promise in advanced therapeutic modalities and integrated systems that provide multimodal capabilities. AI is set to revolutionize clinical decision making and performance of endoscopy.
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Affiliation(s)
- Arif A. Arif
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Shirley X. Jiang
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Michael F. Byrne
- Division of Gastroenterology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
- Satisfai Health, Vancouver, BC, Canada
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16
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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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17
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Otsuka Y, Kamata K. A review of contrast-enhanced harmonic endoscopic ultrasonography for pancreatic solid tumors. J Med Ultrason (2001) 2023:10.1007/s10396-023-01346-3. [PMID: 37584780 DOI: 10.1007/s10396-023-01346-3] [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: 04/22/2023] [Accepted: 06/15/2023] [Indexed: 08/17/2023]
Abstract
Endoscopic ultrasonography (EUS) is superior to other imaging modalities in the detection of pancreatic masses, although differentiating the types of pancreatic masses detected on EUS remains challenging. However, the value of contrast-enhanced harmonic EUS (CH-EUS) using ultrasound contrast agents for this differentiation has been reported. CH-EUS plays a pivotal role in analysis of small lesions that can only be detected with EUS. Recently, CH-EUS was used for staging and/or determining the resectability of pancreatic cancer in several clinical trials. In addition, it is used to estimate the response of pancreatic cancer to chemotherapy and to determine the prognosis in cases of pancreatic cancer and pancreatic neuroendocrine neoplasms. It is also postulated that CH-EUS improves the diagnostic performance of endoscopic ultrasound-guided fine-needle aspiration biopsy (EUS-FNAB) through complementary diagnoses using CH-EUS and EUS-FNAB, or CH-EUS-guided EUS-FNAB. Thus, CH-EUS has been employed for various qualitative diagnoses, including differentiation of pancreatic masses. Second-generation contrast agents such as Sonazoid are used clinically for ultrasound diagnostic imaging of liver and breast disease. The positioning of CH-EUS with Sonazoid as a test for the diagnosis of solid pancreatic tumors is an issue for further studies.
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Affiliation(s)
- Yasuo Otsuka
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, 377-2 Ohno-Higashi, Osaka-Sayama, 589-8511, Japan
| | - Ken Kamata
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, 377-2 Ohno-Higashi, Osaka-Sayama, 589-8511, Japan.
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18
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Rogowska JO, Durko Ł, Malecka-Wojciesko E. The Latest Advancements in Diagnostic Role of Endosonography of Pancreatic Lesions. J Clin Med 2023; 12:4630. [PMID: 37510744 PMCID: PMC10380545 DOI: 10.3390/jcm12144630] [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: 05/18/2023] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Endosonography, a minimally invasive imaging technique, has revolutionized the diagnosis and management of pancreatic diseases. This comprehensive review highlights the latest advancements in endosonography of the pancreas, focusing on key technological developments, procedural techniques, clinical applications and additional techniques, which include real-time elastography endoscopic ultrasound, contrast-enhanced-EUS, EUS-guided fine-needle aspiration or EUS-guided fine-needle biopsy. EUS is well established for T-staging and N-staging of pancreaticobiliary malignancies, for pancreatic cyst discovery, for identifying subepithelial lesions (SEL), for differentiation of benign pancreaticobiliary disorders or for acquisition of tissue by EUS-guided fine-needle aspiration or EUS-guided fine-needle biopsy. This review briefly describes principles and application of EUS and its related techniques.
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Affiliation(s)
| | - Łukasz Durko
- Department of Digestive Tract Diseases, Medical University of Lodz, 90-647 Lodz, Poland
| | - Ewa Malecka-Wojciesko
- Department of Digestive Tract Diseases, Medical University of Lodz, 90-647 Lodz, Poland
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19
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Mann R, Goyal H, Perisetti A. The Role of EUS in Advanced Endoscopic Procedures and Therapeutics-Advancing the Field to Greater Heights. J Clin Med 2023; 12:4557. [PMID: 37510672 PMCID: PMC10380750 DOI: 10.3390/jcm12144557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 06/21/2023] [Indexed: 07/30/2023] Open
Abstract
Endoscopic ultrasound (EUS) provides high-resolution and real-time visualization of various layers of the gastrointestinal (GI) tract and beyond by combining ultrasound technology with endoscopic visualization [...].
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Affiliation(s)
- Rupinder Mann
- Department of Gastroenterology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
| | - Hemant Goyal
- Instructor, Department of Surgery, Division of Endoluminal Surgery & Interventional Gastroenterology, University of Texas Health Science Center, Houston, TX 77030, USA
| | - Abhilash Perisetti
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, 4801 Linwood Blvd, Kansas City, MO 64128, USA
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20
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Rawashdeh B, Kim J, AlRyalat SA, Prasad R, Cooper M. ChatGPT and Artificial Intelligence in Transplantation Research: Is It Always Correct? Cureus 2023; 15:e42150. [PMID: 37602076 PMCID: PMC10438857 DOI: 10.7759/cureus.42150] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/18/2023] [Indexed: 08/22/2023] Open
Abstract
INTRODUCTION ChatGPT (OpenAI, San Francisco, California, United States) is a chatbot powered by language-based artificial intelligence (AI). It generates text based on the information provided by users. It is currently being evaluated in medical research, publishing, and healthcare. However, there has been no prior study on the evaluation of its ability to help in kidney transplant research. This feasibility study aimed to evaluate the application and accuracy of ChatGPT in the field of kidney transplantation. METHODS On two separate dates, February 21 and March 2, 2023, ChatGPT 3.5 was questioned regarding the medical treatment of kidney transplants and related scientific facts. The responses provided by the chatbot were compiled, and a panel of two specialists reviewed the correctness of each answer. RESULTS We demonstrated that ChatGPT possessed substantial general knowledge of kidney transplantation; however, they lacked sufficient information and had inaccurate information that necessitates a deeper understanding of the topic. Moreover, ChatGPT failed to provide references for any of the scientific data it provided regarding kidney transplants, and when requested for references, it provided inaccurate ones. CONCLUSION The results of this short feasibility study indicate that ChatGPT may have the ability to assist in data collecting when a particular query is posed. However, caution should be exercised and it should not be used in isolation as a supplement to research or decisions regarding healthcare because there are still challenges with data accuracy and missing information.
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Affiliation(s)
- Badi Rawashdeh
- Transplant Surgery, Medical College of Wisconsin, Milwaukee, USA
| | - Joohyun Kim
- Transplant Surgery, Medical College of Wisconsin, Milwaukee, USA
| | | | - Raj Prasad
- Transplant Surgery, Medical College of Wisconsin, Milwaukee, USA
| | - Matthew Cooper
- Transplant Surgery, Medical College of Wisconsin, Milwaukee, USA
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21
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Obaid AM, Turki A, Bellaaj H, Ksantini M, AlTaee A, Alaerjan A. Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method. Diagnostics (Basel) 2023; 13:1744. [PMID: 37238227 PMCID: PMC10217597 DOI: 10.3390/diagnostics13101744] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/09/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%.
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Affiliation(s)
- Ahmed Mahdi Obaid
- CEMLab, National School of Electronics and Telecommunications of Sfax, University of Sfax, Sfax 3029, Tunisia
| | - Amina Turki
- CEMLab, National Engineering School of Sfax, University of Sfax, Sfax 3029, Tunisia; (A.T.); (M.K.)
| | - Hatem Bellaaj
- ReDCAD, National Engineering School of Sfax, University of Sfax, Sfax 3029, Tunisia;
| | - Mohamed Ksantini
- CEMLab, National Engineering School of Sfax, University of Sfax, Sfax 3029, Tunisia; (A.T.); (M.K.)
| | | | - Alaa Alaerjan
- College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia;
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22
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Poiraud M, Gkolfakis P, Arvanitakis M. Recent Developments in the Field of Endoscopic Ultrasound for Diagnosis, Staging, and Treatment of Pancreatic Lesions. Cancers (Basel) 2023; 15:cancers15092547. [PMID: 37174012 PMCID: PMC10177103 DOI: 10.3390/cancers15092547] [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: 03/09/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Endoscopic ultrasound (EUS) plays a crucial role in the diagnosis of both solid and cystic pancreatic lesions and in the staging of patients with pancreatic cancer through its use for tissue and fluid sampling. Additionally, in cases of precancerous lesions, EUS-guided therapy can also be provided. This review aims to describe the most recent developments regarding the role of EUS in the diagnosis and staging of pancreatic lesions. Moreover, complementary EUS imaging modalities, the role of artificial intelligence, new devices, and modalities for tissue acquisition, and techniques for EUS-guided treatment are discussed.
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Affiliation(s)
- Marie Poiraud
- Department of Gastroenterology, CUB Erasme Hospital, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Paraskevas Gkolfakis
- Department of Gastroenterology, CUB Erasme Hospital, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Marianna Arvanitakis
- Department of Gastroenterology, CUB Erasme Hospital, Université Libre de Bruxelles, 1070 Brussels, Belgium
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23
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Ye LF, Weng JY, Wu LD. Integrated genomic analysis defines molecular subgroups in dilated cardiomyopathy and identifies novel biomarkers based on machine learning methods. Front Genet 2023; 14:1050696. [PMID: 36824437 PMCID: PMC9941670 DOI: 10.3389/fgene.2023.1050696] [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: 09/22/2022] [Accepted: 01/20/2023] [Indexed: 02/09/2023] Open
Abstract
Aim: As the most common cardiomyopathy, dilated cardiomyopathy (DCM) often leads to progressive heart failure and sudden cardiac death. This study was designed to investigate the molecular subgroups of DCM. Methods: Three datasets of DCM were downloaded from GEO database (GSE17800, GSE79962 and GSE3585). After log2-transformation and background correction with "limma" package in R software, the three datasets were merged into a metadata cohort. The consensus clustering was conducted by the "Consensus Cluster Plus" package to uncover the molecular subgroups of DCM. Moreover, clinical characteristics of different molecular subgroups were compared in detail. We also adopted Weighted gene co-expression network analysis (WGCNA) analysis based on subgroup-specific signatures of gene expression profiles to further explore the specific gene modules of each molecular subgroup and its biological function. Two machine learning methods of LASSO regression algorithm and SVM-RFE algorithm was used to screen out the genetic biomarkers, of which the discriminative ability of molecular subgroups was evaluated by receiver operating characteristic (ROC) curve. Results: Based on the gene expression profiles, heart tissue samples from patients with DCM were clustered into three molecular subgroups. No statistical difference was found in age, body mass index (BMI) and left ventricular internal diameter at end-diastole (LVIDD) among three molecular subgroups. However, the results of left ventricular ejection fraction (LVEF) statistics showed that patients from subgroup 2 had a worse condition than the other group. We found that some of the gene modules (pink, black and grey) in WGCNA analysis were significantly related to cardiac function, and each molecular subgroup had its specific gene modules functions in modulating occurrence and progression of DCM. LASSO regression algorithm and SVM-RFE algorithm was used to further screen out genetic biomarkers of molecular subgroup 2, including TCEAL4, ISG15, RWDD1, ALG5, MRPL20, JTB and LITAF. The results of ROC curves showed that all of the genetic biomarkers had favorable discriminative effectiveness. Conclusion: Patients from different molecular subgroups have their unique gene expression patterns and different clinical characteristics. More personalized treatment under the guidance of gene expression patterns should be realized.
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Affiliation(s)
- Ling-Fang Ye
- Changzhi People’s Hospital, Changzhi, Shanxi, China
| | - Jia-Yi Weng
- Department of Cardiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University,Suzhou, China,*Correspondence: Li-Da Wu, ; Jia-Yi Weng,
| | - Li-Da Wu
- Nanjing Medical University, Nanjing, China,*Correspondence: Li-Da Wu, ; Jia-Yi Weng,
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24
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Perisetti A, Tharian B, Tham TC, Goyal H. Editorial: Recent updates in advanced gastrointestinal endoscopy. Front Med (Lausanne) 2023; 9:1126846. [PMID: 36687446 PMCID: PMC9850213 DOI: 10.3389/fmed.2022.1126846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 12/22/2022] [Indexed: 01/06/2023] Open
Affiliation(s)
- Abhilash Perisetti
- Department of Gastroenterology and Hepatology, Kansas City Veteran Affairs Medical Center, Kansas City, MO, United States,*Correspondence: Abhilash Perisetti ✉ ; ✉
| | - Benjamin Tharian
- Digestive Health Institute, Bayfront Health St. Petersburg Medical Group, St. Petersburg, FL, United States
| | - Tony C. Tham
- Division of Gastroenterology, Ulster Hospital, Dundonald, Belfast, United Kingdom
| | - Hemant Goyal
- Center for Interventional Gastroenterology at UTHealth (iGUT), McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States
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25
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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26
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Dahiya DS, Al-Haddad M, Chandan S, Gangwani MK, Aziz M, Mohan BP, Ramai D, Canakis A, Bapaye J, Sharma N. Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail? J Clin Med 2022; 11:jcm11247476. [PMID: 36556092 PMCID: PMC9786876 DOI: 10.3390/jcm11247476] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Pancreatic cancer is a highly lethal disease associated with significant morbidity and mortality. In the United States (US), the overall 5-year relative survival rate for pancreatic cancer during the 2012-2018 period was 11.5%. However, the cancer stage at diagnosis strongly influences relative survival in these patients. Per the National Cancer Institute (NCI) statistics for 2012-2018, the 5-year relative survival rate for patients with localized disease was 43.9%, while it was 3.1% for patients with distant metastasis. The poor survival rates are primarily due to the late development of clinical signs and symptoms. Hence, early diagnosis is critical in improving treatment outcomes. In recent years, artificial intelligence (AI) has gained immense popularity in gastroenterology. AI-assisted endoscopic ultrasound (EUS) models have been touted as a breakthrough in the early detection of pancreatic cancer. These models may also accurately differentiate pancreatic cancer from chronic pancreatitis and autoimmune pancreatitis, which mimics pancreatic cancer on radiological imaging. In this review, we detail the application of AI-assisted EUS models for pancreatic cancer detection. We also highlight the utility of AI-assisted EUS models in differentiating pancreatic cancer from radiological mimickers. Furthermore, we discuss the current limitations and future applications of AI technology in EUS for pancreatic cancers.
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Affiliation(s)
- Dushyant Singh Dahiya
- Department of Internal Medicine, Central Michigan University College of Medicine, Saginaw, MI 48601, USA
- Correspondence: ; Tel.: +1-(678)-602-1176
| | - Mohammad Al-Haddad
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Saurabh Chandan
- Division of Gastroenterology and Hepatology, CHI Creighton University Medical Center, Omaha, NE 68131, USA
| | - Manesh Kumar Gangwani
- Department of Internal Medicine, The University of Toledo Medical Center, Toledo, OH 43614, USA
| | - Muhammad Aziz
- Department of Gastroenterology, The University of Toledo Medical Center, Toledo, OH 43614, USA
| | - Babu P. Mohan
- Division of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Daryl Ramai
- Division of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Andrew Canakis
- Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Jay Bapaye
- Department of Internal Medicine, Rochester General Hospital, Rochester, NY 14621, USA
| | - Neil Sharma
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Parkview Cancer Institute, Fort Wayne, IN 46845, USA
- Interventional Oncology & Surgical Endoscopy Programs (IOSE), Parkview Health, Fort Wayne, IN 46845, USA
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27
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Pavlidis ET, Sapalidis KG, Pavlidis TE. Modern aspects of the management of pancreatic intraductal papillary mucinous neoplasms: a narrative review. ROMANIAN JOURNAL OF MORPHOLOGY AND EMBRYOLOGY = REVUE ROUMAINE DE MORPHOLOGIE ET EMBRYOLOGIE 2022; 63:491-502. [PMID: 36588487 PMCID: PMC9926151 DOI: 10.47162/rjme.63.3.03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 12/07/2022] [Indexed: 01/02/2023]
Abstract
Intraductal papillary mucinous neoplasms (IPMNs) account for approximately 35% of all cystic tumors in the pancreas and represent the largest subgroup. They are characterized by mucin production and intraductal papillary epithelium growth. IPMNs range from benign to malignant lesions. Biomarkers combined with 18F-Fluorodeoxyglucose-positron emission tomography (18FDG-PET) is the best diagnostic tool. The risk of malignant transformation for main-duct IPMNs is between 34-68% and for low-risk branch-duct (BD)-IPMNs it is 1.1%. Monitoring is crucial for determining the optimal time of surgical excision. Novel artificial intelligence combining clinical, tumor biomarkers, imaging and molecular genomics plays a determinant role in the evaluation of such lesions. The first diagnostic tool is multidetector helical computed tomography (MDHCT) or up-to-date magnetic resonance imaging (MRI). MRI detects malignancy by enhancing mural nodules ≥3 mm. Novel endosonographic interventional techniques have been added to the diagnostic armamentarium. Pancreatoscopy is feasible and effective but challenging for evaluating the diagnosis, invasiveness, and extent of IPMNs. Its findings may change the surgical approach. Pancreatic juice and duodenal fluid have been used recently for molecular biological analysis. The genes most frequently altered include Kirsten rat sarcoma viral proto-oncogene (KRAS), tumor protein p53 (TP53), cyclin-dependent kinase inhibitor 2A (CDKN2A), SMAD family member 4 (SMAD4), and guanine nucleotide-binding protein, alpha stimulating (GNAS). Despite the advances in diagnostic modalities, assessment of this premalignant lesion of pancreatic cancer, with its poor prognosis, is a challenging task. Pancreatectomy is the indicated approach for malignant or high-risk IPMNs with potent malignancy. Conservative management or enucleation for preserving the pancreas of low-risk BD-IPMNs is recommended, but long-term follow-up for recurrence is necessary. The management of IPMNs must be individualized based on preoperative high-risk stigmata and worrisome features.
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Affiliation(s)
- Efstathios T Pavlidis
- School of Medicine, Aristotle University of Thessaloniki, 2nd Propedeutic Department of Surgery, Hippokration Hospital, Thessaloniki, Greece;
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