Detection of large flat colorectal lesions by artificial intelligence: a persistent weakness and blind spot
Computer-aided detection (CADe) has increased adenoma detection in randomised trials. However, unlike other detection adjuncts, CADe is lesion specific, that is, it is trained on a specific set of lesions. If the training does not include sufficient examples of precancerous lesion subsets, CADe may not perform adequately for lesions in that subset. In a prospective assessment of a second-generation CADe programme in 165 colonoscopies, we identified 26 flat lesions ≥10 mm in 17 patients. The endoscopist identified 22 of 26 lesions before the CADe programme. In 13 lesions, the CADe either generated no detection signal or only a signal over part of the lesion after colonoscope position or luminal inflation adjustment. Thus, the second-generation CADe algorithm, like the first generation, frequently fails to effectively detect large flat colorectal lesions, which are likely very important lesions that a CADe programme should identify. The first CADe programme to be launched commercially in
