Effect of computer‐aided colonoscopy on adenoma miss rates and polyp detection: A systematic review and meta‐analysis

Background and Aim Multiple computer‐aided techniques utilizing artificial intelligence (AI) have been created to improve the detection of polyps during colonoscopy and thereby reduce the incidence of colorectal cancer. While adenoma detection rates (ADR) and polyp detection rates (PDR) are importan...

Full description

Saved in:
Bibliographic Details
Published in:Journal of gastroenterology and hepatology Vol. 38; no. 2; pp. 162 - 176
Main Authors: Shah, Sagar, Park, Nathan, Chehade, Nabil El Hage, Chahine, Anastasia, Monachese, Marc, Tiritilli, Amelie, Moosvi, Zain, Ortizo, Ronald, Samarasena, Jason
Format: Journal Article
Language:English
Published: Australia Wiley Subscription Services, Inc 01.02.2023
Subjects:
Adenoma
Adenoma - diagnosis
adenoma detection rate
adenoma miss rate
Artificial Intelligence
Clinical trials
Colonic Polyps - pathology
Colonoscopy
Colonoscopy - methods
Colorectal cancer
Colorectal carcinoma
Colorectal Neoplasms - pathology
Computers
computer‐aided colonoscopy
computer‐aided detection
Humans
Lesions
Meta-analysis
Physical characteristics
polyp detection rate
Polyps
Systematic review
Tumors
adenoma detection rate
polyp detection rate
colorectal cancer
adenoma miss rate
computer-aided colonoscopy
computer-aided detection
artificial intelligence
ISSN:0815-9319, 1440-1746, 1440-1746
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background and Aim Multiple computer‐aided techniques utilizing artificial intelligence (AI) have been created to improve the detection of polyps during colonoscopy and thereby reduce the incidence of colorectal cancer. While adenoma detection rates (ADR) and polyp detection rates (PDR) are important colonoscopy quality indicators, adenoma miss rates (AMR) may better quantify missed lesions, which can ultimately lead to interval colorectal cancer. The purpose of this systematic review and meta‐analysis was to determine the efficacy of computer‐aided colonoscopy (CAC) with respect to AMR, ADR, and PDR in randomized controlled trials. Methods A comprehensive, systematic literature search was performed across multiple databases in September of 2022 to identify randomized, controlled trials that compared CAC with traditional colonoscopy. Primary outcomes were AMR, ADR, and PDR. Results Fourteen studies totaling 10 928 patients were included in the final analysis. There was a 65% reduction in the adenoma miss rate with CAC (OR, 0.35; 95% CI, 0.25–0.49, P < 0.001, I2 = 50%). There was a 78% reduction in the sessile serrated lesion miss rate with CAC (OR, 0.22; 95% CI, 0.08–0.65, P < 0.01, I2 = 0%). There was a 52% increase in ADR in the CAC group compared with the control group (OR, 1.52; 95% CI, 1.39–1.67, P = 0.04, I2 = 47%). There was 93% increase in the number of adenomas > 10 mm detected per colonoscopy with CAC (OR 1.93; 95% CI, 1.18–3.16, P < 0.01, I2 = 0%). Conclusions The results of the present study demonstrate the promise of CAC in improving AMR, ADR, PDR across a spectrum of size and morphological lesion characteristics.
Bibliography:Author contribution
The authors have no conflicts of interest to disclose.
Sagar Shah: Conception and design, analysis and interpretation of the data, drafting of the article, critical revision of the article for important intellectual content, final approval of the article. Nathan Park: conception and design, drafting of the article, critical revision of the article for important intellectual content, final approval of the article. Nabil El Hage Chehade: conception and design, drafting of the article, critical revision of the article for important intellectual content, final approval of the article. Anastasia Chahine: critical revision of the article for important intellectual content, final approval of the article. Marc Monachese: drafting of the article, critical revision of the article for important intellectual content, final approval of the article. Amelie Tiritilli: drafting of the article, critical revision of the article for important intellectual content, final approval of the article. Ronald Ortizo: conception and design, drafting of the article, critical revision of the article for important intellectual content, final approval of the article. Zain Moosvi: conception and design, analysis and interpretation of the data, drafting of the article, critical revision of the article for important intellectual content, final approval of the article. Jason Samarasena: conception and design, analysis and interpretation of the data, drafting of the article, critical revision of the article for important intellectual content, final approval of the article.
Declaration of conflict of interest
All of the above authors agree to be accountable for all aspects of the work.
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ObjectType-Undefined-3
ISSN:0815-9319
1440-1746
1440-1746
DOI:10.1111/jgh.16059