{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:35:48Z","timestamp":1787243748390,"version":"build-2736575974"},"reference-count":95,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T00:00:00Z","timestamp":1675987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Artificial intelligence (AI) is a term that covers a multitude of techniques that are used in a manner that tries to reproduce human intelligence. AI is helpful in various medical specialties that use imaging for diagnostic purposes, and gastroenterology is no exception. In this field, AI has several applications, such as detecting and classifying polyps, detecting the malignancy in polyps, diagnosing Helicobacter pylori infection, gastritis, inflammatory bowel disease, gastric cancer, esophageal neoplasia, and pancreatic and hepatic lesions. The aim of this mini-review is to analyze the currently available studies regarding AI in the field of gastroenterology and hepatology and to discuss its main applications as well as its main limitations.<\/jats:p>","DOI":"10.3390\/diagnostics13040662","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T04:48:03Z","timestamp":1676004483000},"page":"662","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Artificial Intelligence\u2014The Rising Star in the Field of Gastroenterology and Hepatology"],"prefix":"10.3390","volume":"13","author":[{"given":"Madalina","family":"Stan-Ilie","sequence":"first","affiliation":[{"name":"Clinical Hospital, University of Medicine and Pharmacy Carol Davila, 050474 Bucharest, Romania"},{"name":"Gastroenterology Department, Clinical Emergency Hospital of Bucharest, 014461 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6206-6126","authenticated-orcid":false,"given":"Vasile","family":"Sandru","sequence":"additional","affiliation":[{"name":"Gastroenterology Department, Clinical Emergency Hospital of Bucharest, 014461 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5382-4765","authenticated-orcid":false,"given":"Gabriel","family":"Constantinescu","sequence":"additional","affiliation":[{"name":"Clinical Hospital, University of Medicine and Pharmacy Carol Davila, 050474 Bucharest, Romania"},{"name":"Gastroenterology Department, Clinical Emergency Hospital of Bucharest, 014461 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5730-9381","authenticated-orcid":false,"given":"Oana-Mihaela","family":"Plotogea","sequence":"additional","affiliation":[{"name":"Clinical Hospital, University of Medicine and Pharmacy Carol Davila, 050474 Bucharest, Romania"},{"name":"Gastroenterology Department, Clinical Emergency Hospital of Bucharest, 014461 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ecaterina Mihaela","family":"Rinja","sequence":"additional","affiliation":[{"name":"Gastroenterology Department, Clinical Emergency Hospital of Bucharest, 014461 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iulia Florentina","family":"Tincu","sequence":"additional","affiliation":[{"name":"Clinical Hospital, University of Medicine and Pharmacy Carol Davila, 050474 Bucharest, Romania"},{"name":"Gastroenterology Department, \u201cDr Victor Gomoiu\u201d Clinical Children Hospital, 022102 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra","family":"Jichitu","sequence":"additional","affiliation":[{"name":"Gastroenterology Department, Clinical Emergency Hospital of Bucharest, 014461 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriana Elena","family":"Carasel","sequence":"additional","affiliation":[{"name":"Gastroenterology Department, Clinical Emergency Hospital of Bucharest, 014461 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreea Cristina","family":"Butuc","sequence":"additional","affiliation":[{"name":"Gastroenterology Department, Clinical Emergency Hospital of Bucharest, 014461 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bogdan","family":"Popa","sequence":"additional","affiliation":[{"name":"Clinical Hospital, University of Medicine and Pharmacy Carol Davila, 050474 Bucharest, Romania"},{"name":"Gastroenterology Department, Clinical Emergency Hospital of Bucharest, 014461 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4395","DOI":"10.3748\/wjg.v27.i27.4395","article-title":"Role of Artificial Intelligence in Multidisciplinary Imaging Diagnosis of Gastrointestinal Diseases","volume":"27","author":"Nava","year":"2021","journal-title":"World J. Gastroenterol."},{"key":"ref_2","unstructured":"Artificial Intelligence Committee (2021, January 02). House of Lords-AI in the UK: Ready, Willing and Able?. Available online: https:\/\/Publications.Parliament.Uk\/Pa\/Ld201719\/Ldselect\/Ldai\/100\/10005.Html."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.vgie.2020.08.013","article-title":"Artificial Intelligence in Gastrointestinal Endoscopy","volume":"5","author":"Pannala","year":"2020","journal-title":"VideoGIE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1148\/rg.2017170077","article-title":"Deep Learning: A Primer for Radiologists","volume":"37","author":"Chartrand","year":"2017","journal-title":"Radiographics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1136\/gutjnl-2017-314547","article-title":"Real-Time Differentiation of Adenomatous and Hyperplastic Diminutive Colorectal Polyps during Analysis of Unaltered Videos of Standard Colonoscopy Using a Deep Learning Model","volume":"68","author":"Byrne","year":"2019","journal-title":"Gut"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1136\/gutjnl-2018-317500","article-title":"Real-Time Automatic Detection System Increases Colonnoscoopic Polyp and Adenoma Detec-Tion Rates: A Prospective Randomised Controlled Study","volume":"68","author":"Wang","year":"2019","journal-title":"Gut"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1056\/NEJMoa1309086","article-title":"Adenoma Detection Rate and Risk of Colorectal Cancer and Death","volume":"370","author":"Corley","year":"2014","journal-title":"N. Engl. J. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1055\/s-0042-108434","article-title":"Exploring the Clinical Potential of an Automatic Colonic Polyp Detection Method Based on the Creation of Energy Maps","volume":"48","author":"Bernal","year":"2016","journal-title":"Endoscopy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.21037\/atm-21-5081","article-title":"Artificial Intelligence-Assisted Detection and Classification of Colorectal Polyps under Colonoscopy: A Systematic Review and Meta-Analysis","volume":"9","author":"Wang","year":"2021","journal-title":"Ann. Transl. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.gie.2010.07.037","article-title":"Quantitative Analysis and Development of a Computer-Aided System for Identification of Regular Pit Patterns of Colorectal Lesions","volume":"72","author":"Takemura","year":"2010","journal-title":"Gastrointest. Endosc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1055\/s-0029-1243861","article-title":"Computer-Aided Classification of Colorectal Polyps Based on Vascular Patterns: A Pilot Study","volume":"42","author":"Tischendorf","year":"2010","journal-title":"Endoscopy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"357","DOI":"10.7326\/M18-0249","article-title":"Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps during Colon-Oscopy: A Prospective Study","volume":"169","author":"Mori","year":"2018","journal-title":"Ann. Intern. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1016\/j.gie.2014.09.008","article-title":"Novel Computer-Aided Diagnostic System for Colorectal Lesions by Using Endocytoscopy (with Videos)","volume":"81","author":"Mori","year":"2015","journal-title":"Gastrointest. Endosc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.gie.2011.08.051","article-title":"Computer-Aided System for Predicting the Histology of Colorectal Tumors by Using Narrow-Band Imaging Magnifying Colonoscopy (with Video)","volume":"75","author":"Takemura","year":"2012","journal-title":"Gastrointest. Endosc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"28","DOI":"10.21037\/tgh.2019.11.12","article-title":"Endocytoscopy: Technology and Clinical Application in Upper Gastrointestinal Tract","volume":"5","author":"Abad","year":"2020","journal-title":"Transl. Gastroenterol. Hepatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1055\/s-0042-113609","article-title":"Impact of an Automated System for Endocytoscopic Diagnosis of Small Colorectal Lesions: An International Web-Based Study","volume":"48","author":"Mori","year":"2016","journal-title":"Endoscopy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/S0016-5107(96)70135-9","article-title":"Feld Detection of Dysplasia at Colonoscopy Using Laser-Induced Fluorescence: A Blinded Study","volume":"44","author":"Cothren","year":"1996","journal-title":"Gastrointest. Endosc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1111\/j.1751-1097.1991.tb09892.x","article-title":"Spectroscopic Diagnosis of Colonic Dysplasia","volume":"53","author":"Rava","year":"1991","journal-title":"Photochem. Photobiol."},{"key":"ref_19","unstructured":"Wyllie, R., Hyams, J.S., and Kay, M. (2021). Pediatric Gastrointestinal and Liver Disease, Elsevier Health Sciences. [6th ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1097\/MIB.0b013e31829637b9","article-title":"Chromoendoscopy versus Narrow Band Imaging for Colonic Surveillance in Inflammatory Bowel Disease","volume":"19","author":"Efthymiou","year":"2013","journal-title":"Inflamm. Bowel. Dis."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5560","DOI":"10.3748\/wjg.v18.i39.5560","article-title":"Software for Automated Classification of Probe-Based Confocal Laser Endomicroscopy Videos of Colorectal Polyps","volume":"18","author":"Vercauteren","year":"2012","journal-title":"World J. Gastroenterol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1053\/j.gastro.2012.12.003","article-title":"Long-Term Outcomes after Resection for Submucosal Invasive Colorectal Cancers","volume":"144","author":"Ikematsu","year":"2013","journal-title":"Gastroenterology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1055\/s-0033-1344234","article-title":"A Large-Scale Multicenter Study of Long-Term Outcomes after Endoscopic Resection for Submucosal Invasive Colorectal Cancer","volume":"45","author":"Yoda","year":"2013","journal-title":"Endoscopy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1055\/s-0043-102569","article-title":"Colorectal Polypectomy and Endoscopic Mucosal Resection (EMR): European Society of Gastrointestinal Endoscopy (ESGE) Clinical Guideline","volume":"49","author":"Ferlitsch","year":"2017","journal-title":"Endoscopy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/ajg.2016.403","article-title":"Narrow Band Imaging, Magnifying Chromoendoscopy, and Gross Morphological Features for the Optical Diagnosis of T1 Colorectal Cancer and Deep Submucosal Invasion: A Systematic Review and Meta-Analysis","volume":"112","author":"Backes","year":"2017","journal-title":"Am. J. Gastroenterol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1055\/s-0043-105486","article-title":"Accuracy of Diagnosing Invasive Colorectal Cancer Using Computer-Aided Endocytoscopy","volume":"49","author":"Takeda","year":"2017","journal-title":"Endoscopy"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.gie.2018.09.024","article-title":"Fully Automated Diagnostic System with Artificial Intelligence Using Endocytoscopy to Identify the Presence of Histologic Inflammation Associated with Ulcerative Colitis (with Video)","volume":"89","author":"Maeda","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1053\/j.gastro.2020.02.012","article-title":"Development and Validation of a Deep Neural Network for Accurate Evaluation of Endoscopic Images From Patients with Ulcerative Colitis","volume":"158","author":"Takenaka","year":"2020","journal-title":"Gastroenterology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.gie.2019.11.012","article-title":"Deep Learning Algorithms for Automated Detection of Crohn\u2019s Disease Ulcers by Video Capsule Endoscopy","volume":"91","author":"Klang","year":"2020","journal-title":"Gastrointest. Endosc."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Klein, S., Gildenblat, J., Ihle, M.A., Merkelbach-Bruse, S., Noh, K.W., Peifer, M., Quaas, A., and B\u00fcttner, R. (2020). Deep Learning for Sensitive Detection of Helicobacter Pylori in Gastric Biopsies. BMC Gastroenterol., 20.","DOI":"10.1186\/s12876-020-01494-7"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Steinbuss, G., Kriegsmann, K., and Kriegsmann, M. (2020). Identification of Gastritis Subtypes by Convolutional Neuronal Networks on Histological Images of Antrum and Corpus Biopsies. Int. J. Mol. Sci., 21.","DOI":"10.3390\/ijms21186652"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1007\/s10120-018-0793-2","article-title":"Application of Artificial Intelligence Using a Convolutional Neural Network for Detecting Gastric Cancer in Endoscopic Images","volume":"21","author":"Hirasawa","year":"2018","journal-title":"Gastric. Cancer"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1016\/j.gie.2018.11.011","article-title":"Application of Convolutional Neural Network in the Diagnosis of the Invasion Depth of Gastric Cancer Based on Conventional Endoscopy","volume":"89","author":"Zhu","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.gie.2007.08.036","article-title":"Digital Image Analysis of EUS Images Accurately Differentiates Pancreatic Cancer from Chronic Pancreatitis and Normal Tissue","volume":"67","author":"Das","year":"2008","journal-title":"Gastrointest. Endosc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1111\/j.1442-2050.2006.00542.x","article-title":"Survival Outcomes of Resected Patients Who Demonstrate a Pathologic Complete Response after Neoadjuvant Chemoradiation Therapy for Locally Advanced Esophageal Cancer","volume":"19","author":"Hammoud","year":"2006","journal-title":"Dis. Esophagus"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1016\/j.gie.2022.06.013","article-title":"Development of a Deep Learning Model for the Histologic Diagnosis of Dysplasia in Barrett\u2019s Esophagus","volume":"96","author":"Faghani","year":"2022","journal-title":"Gastrointest. Endosc."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.gie.2018.07.037","article-title":"Diagnostic Outcomes of Esophageal Cancer by Artificial Intelligence Using Convolutional Neural Networks","volume":"89","author":"Horie","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1053\/j.gastro.2019.11.030","article-title":"Deep-Learning System Detects Neoplasia in Patients with Barrett\u2019s Esophagus with Higher Accuracy Than Endoscopists in a Multistep Training and Validation Study with Benchmarking","volume":"158","author":"Struyvenberg","year":"2020","journal-title":"Gastroenterology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1038\/labinvest.3700481","article-title":"Computerized Morphometry as an Aid in Determining the Grade of Dysplasia and Progres-Sion to Adenocarcinoma in Barrett\u2019s Esophagus","volume":"86","author":"Sabo","year":"2006","journal-title":"Lab. Investig."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.gie.2017.03.011","article-title":"Computer-Aided Detection of Early Barrett\u2019s Neoplasia Using Volumetric Laser Endomicroscopy","volume":"86","author":"Swager","year":"2017","journal-title":"Gastrointest. Endosc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1016\/j.gie.2013.06.026","article-title":"Wireless Capsule Endoscopy","volume":"78","author":"Wang","year":"2013","journal-title":"Gastrointest. Endosc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1038\/ajg.2011.461","article-title":"Detection of Lesions during Capsule Endoscopy: Physician Performance Is Disappointing","volume":"107","author":"Zheng","year":"2012","journal-title":"Am. J. Gastroenterol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.compbiomed.2016.10.011","article-title":"Generic Feature Learning for Wireless Capsule Endoscopy Analysis","volume":"79","author":"Segui","year":"2016","journal-title":"Comput. Biol. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"E415","DOI":"10.1055\/a-1035-9088","article-title":"CAD-CAP: A 25,000-Image Database Serving the Development of Artificial Intelligence for Capsule Endoscopy","volume":"8","author":"Leenhardt","year":"2020","journal-title":"Endosc. Int. Open"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Jia, X., and Meng, M.Q. (2016, January 16\u201320). A Deep Convolutional Neural Network for Bleeding Detection in Wireless Capsule Endoscopy Images. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590783"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1002\/mp.12147","article-title":"Deep Learning for Polyp Recognition in Wireless Capsule Endoscopy Images","volume":"44","author":"Yuan","year":"2017","journal-title":"Med. Phys."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.gie.2018.10.027","article-title":"Automatic Detection of Erosions and Ulcerations in Wireless Capsule Endoscopy Images Based on a Deep Convolutional Neural Network","volume":"89","author":"Aoki","year":"2019","journal-title":"Gastrointest. Endosc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"165001","DOI":"10.1088\/1361-6560\/aad51c","article-title":"Computer-Aided Detection of Small Intestinal Ulcer and Erosion in Wireless Capsule Endoscopy Images","volume":"63","author":"Fan","year":"2018","journal-title":"Phys. Med. Biol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2379","DOI":"10.1109\/TIP.2018.2801119","article-title":"Hookworm Detection in Wireless Capsule Endoscopy Images with Deep Learning","volume":"27","author":"He","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhu, M., Xu, C., Yu, J., Wu, Y., Li, C., Zhang, M., Jin, Z., and Li, Z. (2013). Differentiation of Pancreatic Cancer and Chronic Pancreatitis Using Computer-Aided Diagnosis of Endoscopic Ultrasound (EUS) Images: A Diagnostic Test. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0063820"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1016\/j.gie.2015.02.043","article-title":"A New Descriptor for Computer-Aided Diagnosis of EUS Imaging to Distinguish Autoimmune Pancreatitis from Chronic Pancreatitis","volume":"82","author":"Zhu","year":"2015","journal-title":"Gastrointest. Endosc."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1159\/000323496","article-title":"Endoscopic Ultrasound Elastography","volume":"11","author":"Giovannini","year":"2011","journal-title":"Pancreatology"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1016\/j.gie.2008.04.031","article-title":"Neural Network Analysis of Dynamic Sequences of EUS Elastography Used for the Differential Diagnosis of Chronic Pancreatitis and Pancreatic Cancer","volume":"68","author":"Vilmann","year":"2008","journal-title":"Gastrointest. Endosc."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","article-title":"Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis","volume":"48","author":"Lambin","year":"2012","journal-title":"Eur. J. Cancer"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: Images Are More than Pictures, They Are Data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1146\/annurev-med-090514-013832","article-title":"Liver Cancer: Connections with Obesity, Fatty Liver, and Cirrhosis","volume":"67","author":"Marengo","year":"2016","journal-title":"Annu. Rev. Med."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5530","DOI":"10.3748\/wjg.v28.i38.5530","article-title":"Ultrasound-Based Artificial Intelligence in Gastroenterology and Hepatology","volume":"28","author":"Liu","year":"2022","journal-title":"World J. Gastroenterol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1428","DOI":"10.1118\/1.4942383","article-title":"A New Computer Aided Diagnosis System for Evaluation of Chronic Liver Disease with Ultrasound Shear Wave Elastography Imaging","volume":"43","author":"Gatos","year":"2016","journal-title":"Med. Phys."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.diii.2019.02.009","article-title":"Diagnosis of Focal Liver Lesions from Ultrasound Using Deep Learning","volume":"100","author":"Schmauch","year":"2019","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1148\/radiol.2017170706","article-title":"Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-Enhanced CT: A Preliminary Study","volume":"286","author":"Yasaka","year":"2018","journal-title":"Radiology"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/978-3-030-00500-9_7","article-title":"Liver Tissue Classification Using an Auto-Context-Based Deep Neural Network with a Multi-Phase Training Framework","volume":"59","author":"Zhang","year":"2018","journal-title":"Patch Based Tech. Med. Imaging"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Jansen, M.J.A., Kuijf, H.J., Veldhuis, W.B., Wessels, F.J., Viergever, M.A., and Pluim, J.P.W. (2019). Automatic Classification of Focal Liver Lesions Based on MRI and Risk Factors. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0217053"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Lara, J., L\u00f3pez-Labrador, F., Gonz\u00e1lez-Candelas, F., Berenguer, M., and Khudyakov, Y.E. (2014). Computational Models of Liver Fibrosis Progression for Hepatitis C Virus Chronic Infection. BMC Bioinform., 15.","DOI":"10.1186\/1471-2105-15-S8-S5"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1002\/hep.30085","article-title":"Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTo) Predicts Outcomes of the Disease: A Derivation and Validation Study Using Machine Learning","volume":"71","author":"Eaton","year":"2020","journal-title":"Hepatology"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1016\/j.jhep.2014.05.039","article-title":"Use of Artificial Intelligence as an Innovative Donor-Recipient Matching Model for Liver Transplantation: Results from a Multicenter Spanish Study","volume":"61","author":"Prieto","year":"2014","journal-title":"J. Hepatol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1002\/lt.24870","article-title":"Validation of Artificial Neural Networks as a Methodology for Donor-Recipient Matching for Liver Transplantation","volume":"24","author":"Ciria","year":"2018","journal-title":"Liver Transpl."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e125","DOI":"10.1097\/TP.0000000000001600","article-title":"Machine-Learning Algorithms Predict Graft Failure after Liver Transplantation","volume":"101","author":"Lau","year":"2017","journal-title":"Transplantation"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1016\/j.mayocp.2018.06.020","article-title":"New-Onset Diabetes and Preexisting Diabetes Are Associated with Comparable Reduction in Long-Term Survival after Liver Transplant: A Machine Learning Approach","volume":"93","author":"Bhat","year":"2018","journal-title":"Mayo Clin."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1111\/den.13875","article-title":"Artificial Intelligence for the Management of Pancreatic Diseases","volume":"32","author":"Gorris","year":"2021","journal-title":"Dig. Endosc."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.pan.2013.11.010","article-title":"The Use of Intelligent Database Systems in Acute Pancreatitis\u2014A Systematic Review","volume":"14","author":"Mittal","year":"2014","journal-title":"Pancreatology"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"6794","DOI":"10.3748\/wjg.v27.i40.6794","article-title":"Artificial Intelligence in Gastroenterology: A State-of-the-Art Review","volume":"27","author":"Engels","year":"2021","journal-title":"World J. Gastroenterol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1097\/MPA.0b013e318258055c","article-title":"Japan Pancreatic Cancer Registry: 30th Year Anniversary","volume":"41","author":"Egawa","year":"2012","journal-title":"Pancreas"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1038\/s41416-019-0694-0","article-title":"Development of PancRISK, a Urine Biomarker-Based Risk Score for Stratified Screening of Pancreatic Cancer Patients","volume":"122","author":"Blyuss","year":"2020","journal-title":"Br. J. Cancer"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1111\/j.1572-0241.2007.01480.x","article-title":"Resectability of Presymptomatic Pancreatic Cancer and Its Relationship to Onset of Diabetes: A Retrospective Review of CT Scans and Fasting Glucose Values Prior to Diagnosis","volume":"102","author":"Takahashi","year":"2007","journal-title":"Am. J. Gastroenterol."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Liu, F., Xie, L., Xia, Y., Fishman, E.K., and Yuille, A.L. (2018). Joint Shape Representation and Classification for Detecting PDAC, ProQuest LLC.","DOI":"10.1007\/978-3-030-32692-0_25"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"101","DOI":"10.4103\/2303-9027.180473","article-title":"Age-Based Computer-Aided Diagnosis Approach for Pancreatic Cancer on Endoscopic Ultrasound Images","volume":"5","author":"Ozkan","year":"2016","journal-title":"Endosc. Ultrasound"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Udri\u0219toiu, A.L., Cazacu, I.M., Gruionu, L.G., Gruionu, G., Iacob, A.V., Burtea, D.E., Ungureanu, B.S., Costache, M.I., Constantin, A., and Popescu, C.F. (2021). Real-Time Computer-Aided Diagnosis of Focal Pancreatic Masses from Endoscopic Ultrasound Imaging Based on a Hybrid Convolutional and Long Short-Term Memory Neural Network Model. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251701"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1159\/000327903","article-title":"Prediction of Severe Acute Pancreatitis at Admission to Hospital Using Artificial Neural Networks","volume":"11","author":"Andersson","year":"2011","journal-title":"Pancreatology"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"e109","DOI":"10.1016\/j.suronc.2011.01.004","article-title":"Primary Pancreatic Cystic Neoplasms Revisited. Part III. Intraductal Papillary Mucinous Neoplasms","volume":"20","author":"Sakorafasa","year":"2011","journal-title":"Surg. Oncol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"5019","DOI":"10.1002\/mp.13159","article-title":"CT Radiomics to Predict High-Risk Intraductal Papillary Mucinous Neoplasms of the Pancreas","volume":"45","author":"Chakraborty","year":"2018","journal-title":"Med. Phys."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Oka, A., Ishimura, N., and Ishihara, S. (2021). A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics, 11.","DOI":"10.3390\/diagnostics11091719"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"E1004","DOI":"10.1055\/a-1475-3624","article-title":"Artificial Intelligence-Assisted Colonic Endocytoscopy for Cancer Recognition: A Multicenter Study","volume":"9","author":"Mori","year":"2021","journal-title":"Endosc. Int. Open"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1016\/j.cgh.2019.09.009","article-title":"Artificial Intelligence-Assisted System Improves Endoscopic Identification of Colorectal Neoplasms","volume":"18","author":"Kudo","year":"2020","journal-title":"Clin. Gastroenterol. Hepatol."},{"key":"ref_85","first-page":"56","article-title":"Automatic Optical Diagnosis of Small Colorectal Lesions by Laser-Induced Autofluorescence","volume":"47","author":"Kuiper","year":"2015","journal-title":"Endoscopy"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1002\/ueg2.12108","article-title":"Current Status and Limitations of Artificial Intelligence in Colonoscopy","volume":"9","author":"Hann","year":"2021","journal-title":"United Eur. Gastroenterol. J."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Neumann, H., Kreft, A., Sivanathan, V., Rahman, F., and Galle, P.R. (2021). Evaluation of Novel LCI CAD EYE System for Real Time Detection of Colon Polyps. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0255955"},{"key":"ref_88","unstructured":"National Cancer Institute (2021, December 01). Surveillance, Epidemiology and End Results Program (SEER), Available online: https:\/\/Seer.Cancer.Gov\/Statfacts\/Html\/Pancreas.Html."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Dumitrescu, E.A., Ungureanu, B.S., Cazacu, I.M., Florescu, L.M., Streba, L., Croitoru, V.M., Sur, D., Croitoru, A., Turcu-Stiolica, A., and Lungulescu, C.V. (2022). Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis. Diagnostics, 12.","DOI":"10.3390\/diagnostics12020309"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00534-002-0752-4","article-title":"The Roles of Endoscopic Ultrasonography in the Diagnosis of Pancreatic Tumors","volume":"11","author":"Maguchi","year":"2004","journal-title":"J. Hepato-Biliary-Pancreat Surg."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1148\/radiology.190.3.8115622","article-title":"Pancreatic Tumors: Evaluation with Endoscopic US, CT, and MR Imaging","volume":"190","author":"Meyenberger","year":"1994","journal-title":"Radiology"},{"key":"ref_92","first-page":"S189","article-title":"Artificial Neural Network for the Prediction of Mortality in Patients Presented with Non-Variceal Upper Gastrointestinal Bleeding","volume":"54","author":"Ungureanu","year":"2022","journal-title":"Endoscopy"},{"key":"ref_93","unstructured":"Messmann, H., Bisschops, R., Antonelli, G., Lib\u00e2nio, D., Sinonquel, P., Abdelrahim, M., Ahmad, O.F., Areia, M., Bergman, J.J.G.H.M., and Bhandari, P. (2022). Mario Dinis-Ribeiro Position Statement on Expected Value of Artificial Intelligence in GI, ESGE."},{"key":"ref_94","first-page":"109","article-title":"Evaluation of a real-time computer-aided polyp detection system during screening colonoscopy: AI-DETECT study","volume":"2","author":"Ahmad","year":"2022","journal-title":"Endoscopy"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Ladabaum, U., Shepard, J., Weng, Y., Desai, M., Singer, S.J., and Mannalithara, A. (2022). Computer-aided detection of polyps does not improve colonoscopist performance in a pragmatic implementation trial. Gastroenterology, S0016\u2013S5085.","DOI":"10.1053\/j.gastro.2022.12.004"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/4\/662\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:29:51Z","timestamp":1760120991000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/4\/662"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,10]]},"references-count":95,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["diagnostics13040662"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13040662","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,10]]}}}