Fluorescence cystoscopy: the end of biopsies for carcinoma in situ detection?

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

BACKGROUND

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.

METHODS

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.

RESULTS

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 sensitivity (accuracy = 0.939, 95% CI = 0.902-0.964; and sensitivity = 0.954, 95% CI = 0.902-0.983) with a short latency of 12 s, much more accurate and quicker than the expert urologists.

CONCLUSIONS

The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.