Optimal bowel preparation is a prerequisite for a successful colonoscopy; however, the rate of inadequate bowel preparation remains relatively high. In this study, we establish a smartphone app that assesses patient bowel preparation using an artificial intelligence (AI)-based prediction system trained on labeled photographs of feces in the toilet and evaluate its impact on bowel preparation quality in colonoscopy outpatients. We conduct a prospective, single-masked, multicenter randomized clinical trial, enrolling outpatients who own a smartphone and are scheduled for a colonoscopy. We screen 578 eligible patients and randomize 524 in a 1:1 ratio to the control or AI-driven app group for bowel preparation. The study endpoints are the percentage of patients with adequate bowel preparation and the total BBPS score, compliance with dietary restrictions and purgative instructions, polyp detection rate, and adenoma detection rate (secondary). The prediction system has an accuracy of 95.15%, a specificity of 97.25%, and an area under the curve of 0.98 in the test dataset. In the full analysis set (n = 500), adequate preparation is significantly higher in the AI-driven app group (88.54 vs. 65.59%; P < 0.001). The mean BBPS score is 6.74 ± 1.25 in the AI-driven app group and 5.97 ± 1.81 in the control group (P < 0.001). The rates of compliance with dietary restrictions (93.68 vs. 83.81%, P = 0.001) and purgative instructions (96.05 vs. 84.62%, P < 0.001) are significantly higher in the AI-driven app group, as is the rate of additional purgative intake (26.88 vs. 17.41%, P = 0.011). Thus, our AI-driven smartphone app significantly improves the quality of bowel preparation and patient compliance.

最佳的肠道准备是成功进行结肠镜检查的先决条件；然而，肠道准备不充分的比例仍然相对较高。在这项研究中，我们建立了一个智能手机应用程序，该应用程序使用基于人工智能 (AI) 的预测系统评估患者的肠道准备情况，该预测系统经过厕所粪便标记照片的训练，并评估其对结肠镜检查门诊患者肠道准备质量的影响。我们进行了一项前瞻性、单盲、多中心随机临床试验，招募了拥有智能手机并计划进行结肠镜检查的门诊患者。我们筛选了 578 名符合条件的患者，并以 1:1 的比例将 524 名患者随机分配到对照组或 AI 驱动的应用程序组进行肠道准备。研究终点是肠道准备充分的患者百分比和 BBPS 总分，遵守饮食限制和泻药说明、息肉检出率和腺瘤检出率（次要）。该预测系统在测试数据集中的准确率为 95.15%，特异性为 97.25%，曲线下面积为 0.98。在全分析集中（n  = 500），充分准备在 AI 驱动的应用程序组中显着更高（88.54 对 65.59%；P  < 0.001）。AI 驱动的应用程序组的平均 BBPS 得分为 6.74 ± 1.25，对照组为 5.97 ± 1.81 ( P  < 0.001)。 在 AI 驱动的应用程序组中，遵守饮食限制（93.68 对 83.81%，P  = 0.001）和泻药指导（96.05 对 84.62%，P < 0.001）的比例明显更高，额外泻药的比例也是如此摄入量（26.88 对 17.41%，P  = 0.011）。因此，我们的人工智能驱动的智能手机应用程序显着提高了肠道准备的质量和患者的依从性。