Performance of Computer-Aided Detection and Quality of Bowel Preparation: A Comprehensive Analysis of Colonoscopy Outcomes

Artificial intelligence (AI) has emerged as a promising tool for detecting and characterizing colorectal polyps during colonoscopy, offering potential enhancements in traditional colonoscopy procedures to improve outcomes in patients with inadequate bowel preparation.BACKGROUNDArtificial intelligenc...

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Published in:Digestive diseases and sciences 2024-09
Main Authors: Norwood, Dalton A, Thakkar, Shyam, Cartee, Amanda, Sarkis, Fayez, Torres-Herman, Tatiana, Montalvan-Sanchez, Eleazar E, Russ, Kirk, Ajayi-Fox, Patricia, Hameed, Anam, Mulki, Ramzi, Sánchez-Luna, Sergio A, Morgan, Douglas R, Peter, Shajan
Format: Article
Language:English
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Summary:Artificial intelligence (AI) has emerged as a promising tool for detecting and characterizing colorectal polyps during colonoscopy, offering potential enhancements in traditional colonoscopy procedures to improve outcomes in patients with inadequate bowel preparation.BACKGROUNDArtificial intelligence (AI) has emerged as a promising tool for detecting and characterizing colorectal polyps during colonoscopy, offering potential enhancements in traditional colonoscopy procedures to improve outcomes in patients with inadequate bowel preparation.This study aimed to assess the impact of an AI tool on computer-aided detection (CADe) assistance during colonoscopy in this population.AIMSThis study aimed to assess the impact of an AI tool on computer-aided detection (CADe) assistance during colonoscopy in this population.This case-control study utilized propensity score matching (PSM) for age, sex, race, and colonoscopy indication to analyze a database of patients who underwent colonoscopy at a single tertiary referral center between 2017 and 2023. Patients were excluded if the procedure was incomplete or aborted owing to poor preparation. The patients were categorized based on the use of AI during colonoscopy. Data on patient demographics and colonoscopy performance metrics were collected. Univariate and multivariate logistic regression models were used to compare the groups.METHODSThis case-control study utilized propensity score matching (PSM) for age, sex, race, and colonoscopy indication to analyze a database of patients who underwent colonoscopy at a single tertiary referral center between 2017 and 2023. Patients were excluded if the procedure was incomplete or aborted owing to poor preparation. The patients were categorized based on the use of AI during colonoscopy. Data on patient demographics and colonoscopy performance metrics were collected. Univariate and multivariate logistic regression models were used to compare the groups.After PSM patients with adequately prepped colonoscopies (n = 1466), the likelihood of detecting hyperplastic polyps (OR = 2.0, 95%CI 1.7-2.5, p 
ISSN:1573-2568
1573-2568
DOI:10.1007/s10620-024-08610-7