Development and Validation of an Artificial Intelligence System for Bowel Preparation Quality Scoring

NCT04487626 | Completed

• 1 other identifier: NCC2020-0159
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

The purpose of this study is to develop and validate the accuracy and reliability of an artificial intelligence(AI) system for bowel preparation quality scoring based of Boston Bowel preparation Scoring(BBPS). Then evaluate whether this AI system can help endoscopists to improve the quality of colonoscopy in clinical practice.

Conditions

Colonic Disease

Keywords

artificial intelligence, deep learning, bowel preparation

Outcome Measures

Primary Outcomes (1)

• accuracy of bowel preparation score

  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.

  6 months

Study Arms (1)

Artificial Intelligence assisted Scoring Group

EXPERIMENTAL

Patients in this group go through colonoscopy under the AI monitoring device.

Device: AI

Interventions

AI DEVICE

After taking standard bowel preparation regimen, patients receive colonoscopy under the AI system device. During the withdrawal phase, bowel preparation quality is assessed by AI-associated scoring system. The withdrawal time is targeted at least 6min. Every detected polyp will be removed and obtained for pathological assessment.

Artificial Intelligence assisted Scoring Group

Eligibility Criteria

• Age: 20 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Patients aged 20-80 years undergoing elective colonoscopy

You may not qualify if:

• Patients received colon or rectal surgery

Sponsors & Collaborators

• National Cancer Center, Korea: lead

Study Sites (1)

National Cancer Center

Goyang-si, Gyeonggi-do, 410-769, South Korea

Location

MeSH Terms

Conditions

Colonic Diseases

Condition Hierarchy (Ancestors)

Intestinal Diseases; Gastrointestinal Diseases; Digestive System Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER GOV
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Staff surgeon

Study Record Dates

• First Submitted: July 22, 2020
• First Posted: July 27, 2020
• Study Start: July 1, 2020
• Primary Completion: October 1, 2020
• Study Completion: December 30, 2020
• Last Updated: March 3, 2021

Record last verified: 2021-03

Data Sharing

• IPD Sharing: Will not share

Locations

KR (1)