ArticleiScience2026
Artificial intelligence diagnostics for bladder tumor identification and grade prediction depend on narrow band imaging cystoscopy.
Article in iScience, 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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10 authors.
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Abstract
The effective treatment of bladder cancer depends on early evaluation through cystoscopy. Given the clinical importance of distinguishing the tumor grade, we report the application of the AI-assisted NBI Cystoscopy Diagnostic System (AINCDS). The AINCDS consists of (1) dual-channel feature extraction module, (2) lesion segmentation module based on feature pyramids, and (3) a multi-task classification module. AINCDS achieved an accuracy for identifying bladder cancer of 0.919 (95% CI = 0.896 to 0.938). For the prediction of tumor grade, the accuracy was 0.764 (95% CI = 0.714 to 0.810). The AINCDS demonstrates similar ability comparable to urologists with over 10 years' experience. With the assistance of AINCDS, the tumor grade prediction accuracy of urologists with 1-3 years' experience improved from 0.667 to 0.793. AINCDS can assist in the diagnosis of bladder cancer and prediction of tumor grade, offering the potential to improve the accuracy of lesion assessment and reduce the workload of urologists.
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