TY - JOUR
T1 - Artificial intelligence in gastroenterology
T2 - A state-of-the-art review
AU - Kröner, Paul T.
AU - Engels, Megan M.L.
AU - Glicksberg, Benjamin S.
AU - Johnson, Kipp W.
AU - Mzaik, Obaie
AU - van Hooft, Jeanin E.
AU - Wallace, Michael B.
AU - El-Serag, Hashem B.
AU - Krittanawong, Chayakrit
N1 - Publisher Copyright:
© The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
PY - 2021/10/28
Y1 - 2021/10/28
N2 - The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
AB - The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett's esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
KW - Artificial intelligence
KW - Clinical applications
KW - Deep learning
KW - Gastroenterology
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85118380972&partnerID=8YFLogxK
U2 - 10.3748/wjg.v27.i40.6794
DO - 10.3748/wjg.v27.i40.6794
M3 - Review article
C2 - 34790008
AN - SCOPUS:85118380972
SN - 1007-9327
VL - 27
SP - 6794
EP - 6824
JO - World Journal of Gastroenterology
JF - World Journal of Gastroenterology
IS - 40
ER -