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Comparative Performance of Artificial Intelligence-Based Computer-Aided Detection Systems for Colorectal Polyps: A
Satoshi Shinozaki1,2, Jun Watanabe3,4,5, Takeshi Kanno6,7
1Shinozaki Medical Clinic, Utsunomiya, Tochigi, Japan.
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.
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.
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