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Artificial Intelligence-assisted Video Colonoscopy for Disease Monitoring of Ulcerative Colitis: A Prospective Study
Noriyuki Ogata1, Yasuharu Maeda1,2, Masashi Misawa1
1Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan.
Backgrounds And Aims:
The Mayo endoscopic subscore [MES] is the most popular endoscopic disease activity measure of ulcerative colitis [UC]. Artificial intelligence [AI]-assisted colonoscopy is expected to reduce diagnostic variability among endoscopists. However, no study has been conducted to ascertain whether AI-based MES assignments can help predict clinical relapse, nor has AI been verified to improve the diagnostic performance of non-specialists.
Methods:
This open-label, prospective cohort study enrolled 110 patients with UC in clinical remission. The AI algorithm was developed using 74 713 images from 898 patients who underwent colonoscopy at three centres. Patients were followed up after colonoscopy for 12 months, and clinical relapse was defined as a partial Mayo score > 2. A multi-video, multi-reader analysis involving 124 videos was conducted to determine whether the AI system reduced the diagnostic variability among six non-specialists.
Results:
The clinical relapse rate for patients with AI-based MES = 1 (24.5% [12/49]) was significantly higher [log-rank test, p = 0.01] than that for patients with AI-based MES = 0 (3.2% [1/31]). Relapse occurred during the 12-month follow-up period in 16.2% [13/80] of patients with AI-based MES = 0 or 1 and 50.0% [10/20] of those with AI-based MES = 2 or 3 [log-rank test, p = 0.03]. Using AI resulted in better inter- and intra-observer reproducibility than endoscopists alone.
Conclusions:
Colonoscopy using the AI-based MES system can stratify the risk of clinical relapse in patients with UC and improve the diagnostic performance of non-specialists.
Insights
Artificial intelligence [AI] can predict ulcerative colitis [UC] relapse using endoscopic scores. AI-assisted colonoscopy improves non-specialist diagnostic accuracy and helps stratify patient relapse risk.
Area of Science:
- Gastroenterology
- Medical Imaging
- Artificial Intelligence
Background:
- The Mayo endoscopic subscore [MES] is the standard for assessing ulcerative colitis [UC] activity.
- Artificial intelligence [AI] in colonoscopy aims to reduce diagnostic variability.
- Previous studies have not evaluated AI's ability to predict clinical relapse or improve non-specialist performance in UC.
Purpose of the Study:
- To determine if AI-based MES assignments can predict clinical relapse in UC patients.
- To assess if AI improves diagnostic performance among non-specialist endoscopists.
- To evaluate AI's impact on inter- and intra-observer variability in MES scoring.
Main Methods:
- A prospective cohort study of 110 UC patients in remission.
- Development of an AI algorithm using 74,713 colonoscopy images from 898 patients.
- 12-month follow-up to define clinical relapse (partial Mayo score > 2) and multi-reader analysis of 124 videos.
Main Results:
- AI-based MES scores of 1 predicted significantly higher relapse rates (24.5%) compared to scores of 0 (3.2%).
- Patients with AI-based MES of 2 or 3 had a 50% relapse rate, versus 16.2% for scores of 0 or 1.
- AI demonstrated improved inter- and intra-observer reproducibility compared to endoscopists alone.
Conclusions:
- AI-assisted colonoscopy can effectively stratify clinical relapse risk in UC patients.
- The AI-based MES system enhances diagnostic accuracy for non-specialist endoscopists.
- AI integration in colonoscopy shows promise for improving UC management.
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