Open Access
CC BY-NC-ND 4.0 · Endoscopy 2021; 09(04): E513-E521
DOI: 10.1055/a-1341-0457
Review

Impact of real-time use of artificial intelligence in improving adenoma detection during colonoscopy: A systematic review and meta-analysis

Munish Ashat
1   Department of Gastroenterology and Hepatology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
,
Jagpal Singh Klair
2   Digestive Disease Institute, Virginia Mason Medical Center, Seattle, Washington, United States
,
Dhruv Singh
3   Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, New York, United States
,
Arvind Rangarajan Murali
1   Department of Gastroenterology and Hepatology, University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
,
Rajesh Krishnamoorthi
2   Digestive Disease Institute, Virginia Mason Medical Center, Seattle, Washington, United States
› Author Affiliations
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Abstract

Background and study aims With the advent of deep neural networks (DNN) learning, the field of artificial intelligence (AI) is rapidly evolving. Recent randomized controlled trials (RCT) have investigated the influence of integrating AI in colonoscopy and its impact on adenoma detection rates (ADRs) and polyp detection rates (PDRs). We performed a systematic review and meta-analysis to reliably assess if the impact is statistically significant enough to warrant the adoption of AI -assisted colonoscopy (AIAC) in clinical practice.

Methods We conducted a comprehensive search of multiple electronic databases and conference proceedings to identify RCTs that compared outcomes between AIAC and conventional colonoscopy (CC). The primary outcome was ADR. The secondary outcomes were PDR and total withdrawal time (WT).

Results Six RCTs (comparing AIAC vs CC) with 5058 individuals undergoing average-risk screening colonoscopy were included in the meta-analysis. ADR was significantly higher with AIAC compared to CC (33.7 % versus 22.9 %; odds ratio (OR) 1.76, 95 % confidence interval (CI) 1.55–2.00; I2 = 28 %). Similarly, PDR was significantly higher with AIAC (45.6 % versus 30.6 %; OR 1.90, 95 %CI, 1.68–2.15, I2 = 0 %). The overall WT was higher for AIAC compared to CC (mean difference [MD] 0.46 (0.00–0.92) minutes, I2 = 94 %).

Conclusions There is an increase in adenoma and polyp detection with the utilization of AIAC.

Supplementary material



Publication History

Received: 24 June 2020

Accepted: 23 November 2020

Article published online:
17 March 2021

© 2021. 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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