Improving bowel preparation for colonoscopy with a smartphone application driven by artificial intelligence

Yan Zhu; Dan-Feng Zhang; Hui-Li Wu; Pei-Yao Fu; Li Feng; Kun Zhuang; Zi-Han Geng; Kun-Kun Li; Xiao-Hong Zhang; Bo-Qun Zhu; Wen-Zheng Qin; Sheng-Li Lin; Zhen Zhang; Tian-Yin Chen; Yuan Huang; Xiao-Yue Xu; Jing-Zheng Liu; Shuo Wang; Wei Zhang; Quan-Lin Li; Ping-Hong Zhou

doi:10.1038/s41746-023-00786-y

Improving bowel preparation for colonoscopy with a smartphone application driven by artificial intelligence

NPJ Digit Med. 2023 Mar 14;6(1):41. doi: 10.1038/s41746-023-00786-y.

Authors

Yan Zhu^#^{1

2}, Dan-Feng Zhang^#^{1

2}, Hui-Li Wu^#³, Pei-Yao Fu^#^{1

2}, Li Feng^#⁴, Kun Zhuang^#⁵, Zi-Han Geng^{1

2}, Kun-Kun Li³, Xiao-Hong Zhang⁴, Bo-Qun Zhu^{1

2}, Wen-Zheng Qin^{1

2}, Sheng-Li Lin^{1

2}, Zhen Zhang^{1

2}, Tian-Yin Chen^{1

2}, Yuan Huang^{1

2}, Xiao-Yue Xu^{1

2}, Jing-Zheng Liu^{1

2}, Shuo Wang^{6

7}, Wei Zhang⁸, Quan-Lin Li^{9

10}, Ping-Hong Zhou^{11

12}

Affiliations

¹ Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
² Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China.
³ Department of Gastroenterology, Zhengzhou Central Hospital, Henan, China.
⁴ Endoscopy Center, Central Hospital of Minhang District, Shanghai, China.
⁵ Department of Gastroenterology, Xian Central Hospital, Shaanxi, China.
⁶ Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
⁷ Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China.
⁸ Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.
⁹ Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China. li.quanlin@zs-hospital.sh.cn.
¹⁰ Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China. li.quanlin@zs-hospital.sh.cn.
¹¹ Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China. zhou.pinghong@zs-hospital.sh.cn.
¹² Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China. zhou.pinghong@zs-hospital.sh.cn.

^# Contributed equally.

Abstract

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.