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Artificial Intelligence for Colonoscopy- Beyond Polyp Detection – A Review of where we are Today and where AI can Take us

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  • Angela Hsu

    (University of Irvine, California Gastroenterology Department, USA)

Abstract

Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide [1]. Colonoscopy with polypectomy is the most effective way to prevent CRC. The best outcome requires a detection rate that approaches the true prevalence of precancerous polyps. Unfortunately, detection rates vary widely among colonoscopists, prompting the development of new technologies and techniques to improve polyp detection. Toward this aim, Artificial Intelligence (AI) for screening and surveillance colonoscopy has emerged, including computer-assisted polyp detection (CADe) and characterization (CADx). On the heels of CADe and CADx are upcoming computer-aided algorithms designed to track colonoscopy quality metrics, which we coin “CAQ.†Examples of CAQ metrics include automated detection of bowel prep score and cecal intubation/withdrawal time, all of which correlate with improved detection. In this review, we describe how the incorporation of CADe, CADx, and CAQ may optimize the colonoscopy outcomes for both patients and physicians.

Suggested Citation

  • Angela Hsu, 2023. "Artificial Intelligence for Colonoscopy- Beyond Polyp Detection – A Review of where we are Today and where AI can Take us," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 49(3), pages 40736-40739, March.
    • Handle: RePEc:abf:journl:v:49:y:2023:i:3:p:40736-40739
    DOI: 10.26717/BJSTR.2023.49.007812
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