An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic Study.

Journal of the National Cancer Institute
Shaoxu WuTianxin Lin


Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. In total, 69,204 images from 10,729 consecutive patients from six hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974-0.979) in the internal validation set and 0.990 (95% CI = 0.979-0.996), 0.982 (95% CI = 0.974-0.988), 0.978 (95% CI = 0.959-0.989), and 0.991 (95% CI = 0.987-0.994) in different external validation sets. In the CAIDS versus urologists' comparisons, the CAIDS showed high accuracy and ...Continue Reading


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