Detection of sessile serrated adenoma using artificial intelligence-enhanced endoscopy: an Asian perspective.

ANZ J Surg

Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, Singapore, Singapore.

Published: March 2024

Background: As the serrated pathway has gained prominence as an alternative colorectal carcinogenesis pathway, sessile serrated adenomas or polyps (SSA/P) have been highlighted as lesions to rule out during colonoscopy. These lesions are however morphologically difficult to detect on endoscopy and can be mistaken for hyperplastic polyps due to similar endoscopic features. With the underlying nature of rapid progression and malignant transformation, interval cancer is a likely consequence of undetected or overlooked SSA/P. Real-time artificial intelligence (AI)-assisted colonoscopy via the computer-assisted detection system (CADe) is an increasingly useful tool in improving adenoma detection rate by providing a second eye during the procedure. In this article, we describe a guide through a video to illustrate the detection of SSA/P during AI-assisted colonoscopy.

Methods: Consultant-grade endoscopists utilized real-time AI-assisted colonoscopy device, as part of a larger prospective study, to detect suspicious lesions which were later histopathologically confirmed to be SSA/P.

Results: All lesions were picked up by the CADe where a real-time green box highlighted suspicious polyps to the clinician. Three SSA/P of varying morphology are described with reference to classical SSA/P features and with comparison to the features of the hyperplastic polyp found in our study. All three SSA/P observed are in keeping with the JNET Classification (Type 1).

Conclusion: In conclusion, CADe is a most useful aid to clinicians during endoscopy in the detection of SSA/P but must be complemented with factors such as good endoscopy skill and bowel prep for effective detection, and biopsy coupled with subsequent accurate histological diagnosis.

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Source
http://dx.doi.org/10.1111/ans.18785DOI Listing

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