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Comparative Performance of Artificial Intelligence-Based Computer-Aided Detection Systems for Colorectal Polyps: A

Satoshi Shinozaki^1,2, Jun Watanabe^3,4,5, Takeshi Kanno^6,7

¹Shinozaki Medical Clinic, Utsunomiya, Tochigi, Japan.

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Summary

Computer-aided detection (CADe) systems show promise in improving adenoma detection rates (ADRs) during colonoscopy. Several CADe systems demonstrated statistically significant improvements in ADRs compared to standard colonoscopy.

Keywords:

artificial intelligence colonoscopy colorectal polyp computer‐aided detection endoscopy

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Area of Science:

Gastroenterology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Adenoma detection rate (ADR) is a key quality indicator in colonoscopy.
Computer-aided detection (CADe) systems are being developed to enhance polyp detection.
Optimizing ADR is crucial for colorectal cancer prevention.

Purpose of the Study:

To systematically evaluate the effectiveness of CADe systems in improving ADR.
To compare the performance of various CADe systems against standard colonoscopy.
To assess the impact of CADe on sessile serrated lesion detection rates (SSLDRs).

Main Methods:

Bayesian network meta-analysis of randomized controlled trials (RCTs).
Systematic literature search across MEDLINE, Embase, and Cochrane Central Register.
Primary outcome: adenoma detection rate (ADR); Secondary outcome: sessile serrated lesion detection rate (SSLDR).

Main Results:

48 RCTs involving 38,986 patients were analyzed.
ENDO-AID, CADEYE, and GI Genius showed improved ADR compared to controls (moderate confidence).
ENDO-AID and GI Genius may improve SSLDR detection.

Conclusions:

Certain CADe systems are likely to enhance ADR.
Evidence certainty ranges from low to moderate based on the CINeMA framework.
CADe represents a potential advancement in colonoscopic surveillance.