CC BY-NC-ND 4.0 · Endoscopy 2022; 54(10): 972-979
DOI: 10.1055/a-1799-8297
Original article

Artificial intelligence-based assessments of colonoscopic withdrawal technique: a new method for measuring and enhancing the quality of fold examination

Wei Liu*
1   Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
,
Yu Wu*
2   Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
,
Xianglei Yuan
1   Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
,
Jingyu Zhang
3   State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, Sichuan, China
,
Yao Zhou
2   Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
,
4   Department of Gastroenterology, Cangxi Peopleʼs Hospital, Guangyuan, Sichuan, China
,
Peipei Zhu
5   Department of Gastroenterology, Dazhou Integrated Traditional Chinese and Western Medicine Hosptial, Dazhou, Sichuan, China
,
6   Department of Gastroenterology, Nanchong Central Hospital, Nanchong, Sichuan, China
,
Long He
1   Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
,
Bing Hu
1   Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
,
Zhang Yi
2   Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
› Author Affiliations
Supported by: China Postdoctoral Science Foundation 2021M702341
Supported by: National Natural Science Foundation of China 82170675
Supported by: 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University ZYJC21011


Abstract

Background This study aimed to develop an artificial intelligence (AI)-based system for measuring fold examination quality (FEQ) of colonoscopic withdrawal technique. We also examined the relationship between the system’s evaluation of FEQ and FEQ scores from experts, and adenoma detection rate (ADR) and withdrawal time of colonoscopists, and evaluated the system’s ability to improve FEQ during colonoscopy.

Methods First, we developed an AI-based system for measuring FEQ. Next, 103 consecutive colonoscopies performed by 11 colonoscopists were collected for evaluation. Three experts graded FEQ of each colonoscopy, after which the recorded colonoscopies were evaluated by the system. We further assessed the system by correlating its evaluation of FEQ against expert scoring, historical ADR, and withdrawal time of each colonoscopist. We also conducted a prospective observational study to evaluate the systemʼs performance in enhancing fold examination.

Results The system’s evaluations of FEQ of each endoscopist were significantly correlated with expertsʼ scores (r = 0.871, P < 0.001), historical ADR (r = 0.852, P = 0.001), and withdrawal time (r = 0.727, P = 0.01). For colonoscopies performed by colonoscopists with previously low ADRs (< 25 %), AI assistance significantly improved the FEQ, evaluated by both the AI system (0.29 [interquartile range (IQR) 0.27–0.30] vs. 0.23 [0.17–0.26]) and experts (14.00 [14.00–15.00] vs. 11.67 [10.00–13.33]) (both P < 0.001).

Conclusion The system’s evaluation of FEQ was strongly correlated with FEQ scores from experts, historical ADR, and withdrawal time of each colonoscopist. The system has the potential to enhance FEQ.

* Co-first authors


Figs. 1 s–3 s, Tables 1 s-4 s



Publication History

Received: 03 August 2021

Accepted: 10 February 2022

Article published online:
07 April 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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