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

Not Applicable

• Conditions: Bowel Preparation
• Interventions: Device: Artificial intelligence assisted bowel preparation quality scoring system; Device: Conventional human scoring

• Registration Number: NCT03908645
• Lead Sponsor: Shandong University

Brief 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.

Study Timeline

• Study Start: 2018-12-15
• Status Verified: 2019-04
• Study First Submitted: 2019-04-08
• Study First Submitted QC: 2019-04-08
• Last Update Submitted: 2019-04-08
• Study First Posted: 2019-04-09
• Last Update Posted: 2019-04-09
• Primary Completion: 2019-12-15
• Study Completion: 2020-04-15

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 100

Inclusion Criteria

• Patients aged 18-70 years undergoing afternoon colonoscopy

Exclusion Criteria

• Known or suspected bowel obstruction, stricture or perforation
• Compromised swallowing reflex or mental status
• Severe chronic renal failure(creatinine clearance < 30 ml/min)
• Severe congestive heart failure (New York Heart Association class III or IV)
• Uncontrolled hypertension (systolic blood pressure > 170 mm Hg, diastolic blood pressure > 100 mm Hg)
• Dehydration
• Disturbance of electrolytes
• Pregnancy or lactation
• Hemodynamically unstable
• Unable to give informed consent

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Artificial Intelligence assisted Scoring Group	Artificial intelligence assisted bowel preparation quality scoring system	Patients in this group go through colonoscopy under the AI monitoring device.
Conventional Human Scoring Group	Conventional human scoring	Patients in this group go through conventional colonoscopy without AI monitoring device.

Primary Outcome Measures

Name	Time	Method
The rate of patients achieving adequate bowel preparation in each group.	6 months	Bowel preparation quality was measured by BBPS. After fully washing or suctioning of colonic contents, three segments including right colon (containing cecum and ascending colon), transvers colon (containing hepatic and splenic flexures) and left colon (containing descending and sigmoid colon) were individually scored from 0 to 3. Point 0 refers to unprepared colon segment with obscured solid stool making mucosa cannot be seen; Point 1 refers to part of mucosa can be seen, but some areas are covered by staining, residual stool, and/or opaque liquid; Point 2 refers to entire mucosa is well-seen; Point 3 refers to clean colon segment without staining, fecal materials or liquids. A sub-score of each colon segment was used, ranging from minimum 0 to maximum 3. The highest score means the excellent bowel preparation. Adequate bowel preparation was defined as a total BBPS≥6 and sub-BBPS≥2 per segment.

Secondary Outcome Measures

Name	Time	Method
Adenoma Detection Rate	6 months	The proportion of patients from whom at least one adenoma can be detected.
Polyp Detection Rate	6 months	The proportion of patients from whom at least one polyp can be detected.

Trial Locations

Locations (1)

Qilu hosipital; 🇨🇳 Jinan, Shandong, China