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

Background 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. Aims This study aime...

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Bibliographic Details
Published in:Digestive diseases and sciences 2024-10, Vol.69 (10), p.3681-3689
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
Subjects:
Adenoma - diagnosis
Adenoma - diagnostic imaging
Adult
Aged
Artificial Intelligence
Biochemistry
Case-Control Studies
Cathartics - administration & dosage
Colonic Polyps - diagnosis
Colonic Polyps - diagnostic imaging
Colonic Polyps - pathology
Colonoscopy
Colonoscopy - methods
Colonoscopy - standards
Colorectal Neoplasms - diagnosis
Colorectal Neoplasms - diagnostic imaging
Diagnosis, Computer-Assisted - methods
Female
Gastroenterology
Hepatology
Humans
Male
Medicine
Medicine & Public Health
Middle Aged
Oncology
Original
Original Article
Patients
Polyps
Retrospective Studies
Transplant Surgery
Tumors
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Summary:Background 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. Aims This study aimed to assess the impact of an AI tool on computer-aided detection (CADe) assistance during colonoscopy in this population. Methods 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. Results 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:0163-2116
1573-2568
1573-2568
DOI:10.1007/s10620-024-08610-7