Ogata Noriyuki, Maeda Yasuharu, Misawa Masashi, Takenaka Kento, Takabayashi Kaoru, Iacucci Marietta, Kuroki Takanori, Takishima Kazumi, Sasabe Keisuke, Niimura Yu, Kawashima Jiro, Ogawa Yushi, Ichimasa Katsuro, Nakamura Hiroki, Matsudaira Shingo, Sasanuma Seiko, Hayashi Takemasa, Wakamura Kunihiko, Miyachi Hideyuki, Baba Toshiyuki, Mori Yuichi, Ohtsuka Kazuo, Ogata Haruhiko, Kudo Shin-Ei
Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan.
APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.
J Crohns Colitis. 2025 Jan 11;19(1). doi: 10.1093/ecco-jcc/jjae080.
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.
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.
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.
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.
梅奥内镜亚评分[MES]是溃疡性结肠炎[UC]最常用的内镜下疾病活动度评估指标。人工智能[AI]辅助结肠镜检查有望减少内镜医师之间的诊断差异。然而，尚未有研究确定基于AI的MES评分能否帮助预测临床复发，也未证实AI能提高非专科医生的诊断性能。
这项开放标签的前瞻性队列研究纳入了110例临床缓解期的UC患者。AI算法是利用来自三个中心的898例接受结肠镜检查患者的74713张图像开发的。患者在结肠镜检查后随访12个月，临床复发定义为梅奥部分评分>2。进行了一项涉及124个视频的多视频、多观察者分析，以确定AI系统是否减少了6名非专科医生之间的诊断差异。
基于AI的MES=1的患者临床复发率（24.5%[12/49]）显著高于基于AI的MES=0的患者（3.2%[1/31]）[对数秩检验，p=0.01]。在12个月的随访期内，基于AI的MES=0或1的患者中有16.2%[13/80]复发，而基于AI的MES=2或3的患者中有50.0%[10/20]复发[对数秩检验，p=0.03]。与仅由内镜医师操作相比，使用AI可提高观察者间和观察者内的可重复性。
使用基于AI的MES系统进行结肠镜检查可以对UC患者的临床复发风险进行分层，并提高非专科医生的诊断性能。
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