SynthesisFrontiers in artificial intelligence2026
Artificial intelligence-based computer-aided detection systems for adenomas during colonoscopy: a systematic review and Bayesian network meta-analysis.
Synthesis in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Background and aims: Artificial intelligence-based computer-aided detection (CADe) systems have been developed to enhance the adenoma detection rate (ADR) during colonoscopy, but their performance is unknown. We primarily aimed to compare the effectiveness of each CADe system with conventional colonoscopy (CC). As a secondary objective, we performed an exploratory comparison among different CADe systems. Methods: A systematic literature search of 6 databases was conducted to find randomized controlled trials (RCTs) evaluating the use of CADe systems during colonoscopy. A Bayesian network meta-analysis was performed on the included studies using R 4.5.2 software. The primary outcome was ADR. Results: A total of 21 RCTs involving 19,006 participants were included. Nine CADe systems were compared. For ADR based on modified intention-to-treat (ADR-mITT), DEEP Conclusion: Compared with CC, the CADe system significantly improved ADR. However, given the limited direct comparative evidence and the star-shaped network, the results, especially the differences among systems, should be interpreted cautiously. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420261290518, identifier: CRD420261290518.
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