TY - JOUR
T1 - Artificial intelligence-assisted colonoscopy to identify histologic remission and predict the outcomes of patients with ulcerative colitis
T2 - A systematic review
AU - Maeda, Yasuharu
AU - Kudo, Shin ei
AU - Santacroce, Giovanni
AU - Ogata, Noriyuki
AU - Misawa, Masashi
AU - Iacucci, Marietta
N1 - Publisher Copyright:
© 2024 Editrice Gastroenterologica Italiana S.r.l.
PY - 2024/7
Y1 - 2024/7
N2 - This systematic review evaluated the current status of AI-assisted colonoscopy to identify histologic remission and predict the clinical outcomes of patients with ulcerative colitis. The use of artificial intelligence (AI) has increased substantially across several medical fields, including gastrointestinal endoscopy. Evidence suggests that it may be helpful to predict histologic remission and relapse, which would be beneficial because current histological diagnosis is limited by the inconvenience of obtaining biopsies and the high cost and time-intensiveness of pathological diagnosis. MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 1, 2000, and October 31, 2023. Nine studies fulfilled the selection criteria and were included; five evaluated the prediction of histologic remission, two assessed the prediction of clinical outcomes, and two evaluated both. Seven were prospective observational or cohort studies, while two were retrospective observational studies. No randomized controlled trials were identified. AI-assisted colonoscopy demonstrated sensitivity between 65 %–98 % and specificity values of 80 %–97 % for identifying histologic remission. Furthermore, it was able to predict future relapse in patients with ulcerative colitis. However, several challenges and barriers still exist to its routine clinical application, which should be overcome before the true potential of AI-assisted colonoscopy can be fully realized.
AB - This systematic review evaluated the current status of AI-assisted colonoscopy to identify histologic remission and predict the clinical outcomes of patients with ulcerative colitis. The use of artificial intelligence (AI) has increased substantially across several medical fields, including gastrointestinal endoscopy. Evidence suggests that it may be helpful to predict histologic remission and relapse, which would be beneficial because current histological diagnosis is limited by the inconvenience of obtaining biopsies and the high cost and time-intensiveness of pathological diagnosis. MEDLINE and the Cochrane Central Register of Controlled Trials were searched for studies published between January 1, 2000, and October 31, 2023. Nine studies fulfilled the selection criteria and were included; five evaluated the prediction of histologic remission, two assessed the prediction of clinical outcomes, and two evaluated both. Seven were prospective observational or cohort studies, while two were retrospective observational studies. No randomized controlled trials were identified. AI-assisted colonoscopy demonstrated sensitivity between 65 %–98 % and specificity values of 80 %–97 % for identifying histologic remission. Furthermore, it was able to predict future relapse in patients with ulcerative colitis. However, several challenges and barriers still exist to its routine clinical application, which should be overcome before the true potential of AI-assisted colonoscopy can be fully realized.
KW - Clinical relapse
KW - Convolution neural network
KW - Inflammatory bowel disease
KW - Mucosal healing
UR - https://www.scopus.com/pages/publications/85190738599
U2 - 10.1016/j.dld.2024.04.005
DO - 10.1016/j.dld.2024.04.005
M3 - Article
C2 - 38643020
AN - SCOPUS:85190738599
SN - 1590-8658
VL - 56
SP - 1119
EP - 1125
JO - Digestive and Liver Disease
JF - Digestive and Liver Disease
IS - 7
ER -