Use of an AI Image Recognition-Based Smartphone App in Personalized Bowel Preparation Before Colonoscopy: Prospective Randomized Controlled Trial.

IF 6.3 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Fei Han, Shilin Gao, Hanqiang Zhan, Nan Wang, Zhaoxiang Song, Jinlin Xie, Yuhan Hou, Yimeng Deng, Jianning Yao
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引用次数: 0

Abstract

Background: Sufficient bowel preparation is critical for increasing the quality of colonoscopy. However, current bowel preparation guidelines have limitations. We used a constructed and validated convolutional neural network model to assist patients in assessing the adequacy of bowel preparation and guide the use of laxatives based on individual variations.

Objective: This research intended to explore the influence of AI-guided bowel preparation on patients' use of laxatives and bowel cleansing quality.

Methods: We conducted a prospective randomized controlled trial that included patients who had a colonoscopy at the First Affiliated Hospital of Zhengzhou University and used sodium phosphate solution for bowel cleansing. Included individuals were randomly categorized into 2 groups: the AI-guided group, which used an AI program to assess the adequacy of bowel cleansing and stopped the use of laxatives once the assessment was passed, and the control group, which evaluated bowel preparation adequacy based on standard practices and used the recommended doses of laxatives. The primary outcomes were the amount of laxatives used and the rate of adequate bowel cleansing based on the Boston Bowel Preparation Scale (BBPS). Other outcomes included overall BBPS scores, BBPS scores by colon region, adenoma detection rate (ADR), polyp detection rate, and advanced ADR, as well as adverse reactions and compliance.

Results: This study included 513 patients (AI-guided group: n=255, 49.7%; control group: n=258, 50.3%). The volume of bowel-cleansing solution consumed by patients in the AI-guided group was 1245.5 (SD 377.2) mL, significantly lower than the 1586.2 (SD 50.4) mL in the control group (P<.001). Reduced use of laxatives was independently predicted by younger age, lower BMI, and higher defecation frequency. The rate of adequate bowel preparation in the AI group was not inferior to that in the control group (238/255, 93.3% vs 229/258, 88.8%, respectively; 1-sided P<.001 for noninferiority). No significant differences were observed in segmental or total BBPS scores, ADR, polyp detection rate, and advanced ADR between the 2 groups. Additionally, the medication adherence rate in the AI-guided group was significantly higher than in the control group (247/255, 96.9% vs 236/258, 91.5%, respectively; P=.009), whereas the incidence of adverse reactions was notably lower (5/255, 2% vs 15/258, 5.8%, respectively; P=.02).

Conclusions: Using the AI-driven bowel preparation program to assist patients in personalized bowel preparation can reduce unnecessary use of laxatives while achieving results comparable to those of standard practices.

Trial registration: ClinicalTrials.gov NCT06610630; https://clinicaltrials.gov/study/NCT06610630.

在结肠镜检查前使用基于人工智能图像识别的智能手机应用程序进行个性化肠道准备:前瞻性随机对照试验。
背景:充分的肠道准备是提高结肠镜检查质量的关键。然而,目前的肠道准备指南有局限性。我们使用构建和验证的卷积神经网络模型来帮助患者评估肠道准备的充分性,并根据个体差异指导泻药的使用。目的:探讨人工智能引导下的肠道准备对患者使用泻药及肠道清洁质量的影响。方法:我们进行了一项前瞻性随机对照试验,纳入了在郑州大学第一附属医院进行结肠镜检查并使用磷酸钠溶液进行肠道清洁的患者。纳入的个体被随机分为两组:人工智能引导组,使用人工智能程序评估肠道清洁的充分性,一旦评估通过就停止使用泻药;对照组,根据标准做法评估肠道准备的充分性,并使用推荐的泻药剂量。主要结果是使用泻药的量和基于波士顿肠道准备量表(BBPS)的适当肠道清洁率。其他结果包括总体BBPS评分、结肠区域BBPS评分、腺瘤检出率(ADR)、息肉检出率、晚期ADR,以及不良反应和依从性。结果:本研究纳入513例患者,其中ai引导组:n=255,占49.7%;对照组:n=258,占50.3%。人工智能引导组患者的清肠液用量为1245.5 (SD 377.2) mL,显著低于对照组的1586.2 (SD 50.4) mL。结论:使用人工智能驱动的肠道准备程序协助患者进行个性化肠道准备,可以减少不必要的泻药使用,同时达到与标准做法相当的效果。试验注册:ClinicalTrials.gov NCT06610630;https://clinicaltrials.gov/study/NCT06610630。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
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