Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study,Gastrointestinal Endoscopy

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Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study
Gastrointestinal Endoscopy ( IF 6.7 ) Pub Date : 2021-10-22 , DOI: 10.1016/j.gie.2021.10.019
Yasuharu Maeda ₁ , Shin-Ei Kudo ₁ , Noriyuki Ogata ₁ , Masashi Misawa ₁ , Marietta Iacucci ₂ , Mayumi Homma ₃ , Tetsuo Nemoto ₃ , Kazumi Takishima ₁ , Kentaro Mochida ₁ , Hideyuki Miyachi ₁ , Toshiyuki Baba ₁ , Kensaku Mori ₄ , Kazuo Ohtsuka ₅ , Yuichi Mori ₆

Affiliation

Background and Aims

The use of artificial intelligence (AI) during colonoscopy is attracting attention as an endoscopist-independent tool to predict histologic disease activity of ulcerative colitis (UC). However, no study has evaluated the real-time use of AI to directly predict clinical relapse of UC. Hence, it is unclear whether the real-time use of AI during colonoscopy helps clinicians make real-time decisions regarding treatment interventions for patients with UC. This study aimed to establish the role of real-time AI in stratifying the relapse risk of patients with UC in clinical remission.

Methods

This open-label, prospective, cohort study was conducted in a referral center. The cohort comprised 145 consecutive patients with UC in clinical remission who underwent AI-assisted colonoscopy with a contact-microscopy function. We classified patients into either the Healing group or Active group based on the AI outputs during colonoscopy. The primary outcome measure was clinical relapse of UC (defined as a partial Mayo score >2) during 12 months of follow-up after colonoscopy.

Results

Overall, 135 patients completed the 12-month follow-up after AI-assisted colonoscopy. AI-assisted colonoscopy classified 61 patients as the Healing group and 74 as the Active group. The relapse rate was significantly higher in the AI-Active group (28.4% [21/74]; 95% confidence interval, 18.5%-40.1%) than in the AI-Healing group (4.9% [3/61]; 95% confidence interval, 1.0%-13.7%; P < .001).

Conclusions

Real-time use of AI predicts the risk of clinical relapse in patients with UC in clinical remission, which helps clinicians make real-time decisions regarding treatment interventions. (Clinical trial registration number: UMIN000036650.)

中文翻译：

结肠镜检查中实时使用人工智能预测溃疡性结肠炎复发的前瞻性研究

背景和目标

在结肠镜检查期间使用人工智能 (AI) 作为一种独立于内镜医师的工具来预测溃疡性结肠炎 (UC) 的组织学疾病活动，正引起人们的关注。然而，没有研究评估实时使用 AI 直接预测 UC 的临床复发。因此，尚不清楚在结肠镜检查期间实时使用 AI 是否有助于临床医生对 UC 患者的治疗干预做出实时决策。本研究旨在确定实时 AI 在临床缓解 UC 患者复发风险分层中的作用。

方法

这项开放标签、前瞻性、队列研究是在转诊中心进行的。该队列包括 145 名临床缓解的连续 UC 患者，他们接受了具有接触显微镜功能的 AI 辅助结肠镜检查。我们根据结肠镜检查期间的 AI 输出将患者分为治愈组或活跃组。主要结局指标是结肠镜检查后 12 个月随访期间 UC 的临床复发（定义为部分 Mayo 评分 >2）。

结果

总体而言，135 名患者在 AI 辅助结肠镜检查后完成了 12 个月的随访。AI 辅助结肠镜检查将 61 名患者归为愈合组，将 74 名患者归为活跃组。AI-Active 组的复发率（28.4% [21/74]；95% 置信区间，18.5%-40.1%）明显高于 AI-Healing 组（4.9% [3/61]；95%）置信区间，1.0%-13.7%；P < .001）。