NCT03908645

Development and Validation of a Deep Learning Algorithm for Bowel Preparation Quality Scoring

• Status: Unknown
• Phase: N/A
• Study type: Interventional
• Enrollment: 100 (estimated)
• Sponsor: Shandong University · Academic / Other
• Sex: All
• Age: 18 Years – 70 Years
• Healthy volunteers: Not accepted

Summary

The purpose of this study is to develop and validate the performance of an artificial intelligence(AI) assisted Boston Bowel preparation Scoring(BBPS) system for evaluation of bowel cleanness, then testify whether this new scoring system can help physicians to improve the quality control parameters of colonoscopy in clinic practice.

Detailed description

Colonoscopy is recommended as a routine examination for colorectal cancer screening. Adequate bowel preparation is indispensable to ensure a clear vision of colonic mucosa，complete inspection of all colon segments, and furthermore improves the detection rates of small adenomas. Thus, the adequacy of bowel preparation should be accurately evaluated and documented. However, the accuracy of current bowel preparation quality scales greatly relies on intra-observer and inter-observer consistency for lack of objective measurements. Recently, deep learning based on central neural networks (CNN) has shown multiple potential in computer-aided detection and computer-aided diagnose of gastrointestinal lesions. While, no studies have been conducted to evaluate the performance of deep learning algorithm in bowel preparation quality scoring. This study aims to train an algorithm to assess bowel preparation quality using the BBPS, and testify whether the engagement of AI can improve the quality control parameters of colonoscopy.

Conditions

• Bowel Preparation

Interventions

Type	Name	Description
DEVICE	Artificial intelligence assisted bowel preparation quality scoring system	After receiving standard bowel preparation regimen, patients go through colonoscopy under the AI monitoring device. During the withdrawal process, bowel preparation quality is monitored by AI-associated scoring system. Whenever a sub-score below 2 points is detected, endoscopist will be alarmed up to three times to wash and suck the colonic contents. Videos will be recorded and re-evaluated by experts to determine the final BBPS score. The withdrawal time is targeted at least 6min in accordance with colonoscopy quality practice. All detected polyps will be removed and obtained for histological assessment, with the possible exception of diminutive(less than 5mm) rectal polyps.
DEVICE	Conventional human scoring	After receiving standard bowel preparation regimen, patients go through conventional colonoscopy without the AI monitoring device. During the withdrawal process, after washing and sucking the colonic contents according to endoscopist's personal experience, bowel preparation quality is evaluated by human. Videos will be recorded and re-evaluated by experts to determine the final BBPS score. The withdrawal time is targeted at least 6min in accordance with colonoscopy quality practice. All detected polyps will be removed and obtained for histological assessment, with the possible exception of diminutive(less than 5mm) rectal polyps.

Timeline

• Start date: 2018-12-15
• Primary completion: 2019-12-15
• Completion: 2020-04-15
• First posted: 2019-04-09
• Last updated: 2019-04-09

Locations

1 site across 1 country: China