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Study of bladder cancer detection in standard white light versus AI-supported endoscopy-01 (RAISE-01) - Development
Peter B Hjort1, Jacob E Jensen2, Jørgen B Jensen3
1Department of Clinical Medicine, Aarhus University, Palle Juul-Jensens Blvd. 82, 8200 Aarhus N, Denmark; Department of Urology, Aarhus University Hospital, Palle Juul-Jensens Blvd. 35, 8200 Aarhus N, Denmark.
Background:
Diagnosis and surveillance of bladder cancer rely on white-light cystoscopy (WLC). However, this modality is operator-dependent and associated with a risk of missed lesions, contributing to high recurrence rates, especially in non-muscle invasive bladder cancer. Recent advances in artificial intelligence (AI) enable software-based decision support for bladder lesion detection, with potential for vendor-independent deployment and broad integration into routine clinical workflows.
Objective:
To develop and externally validate an AI-based clinical decision support system for real-time bladder lesion detection during cystoscopy.
Methods:
CystoAID, a convolutional neural network-based object detection system, was trained on prospectively collected video recordings from flexible cystoscopies and transurethral resections of bladder tumors. Diagnostic accuracy was evaluated using a retrospective external validation dataset representative of routine clinical practice, in accordance with STARD-AI recommendations.
Results:
In the external validation cohort, CystoAID achieved a sensitivity of 1.00 (95% CI 0.95-1.00). Precision was 88.1% (95% CI 81.3-92.7), exceeding published estimates for WLC. Precision-recall analysis showed consistently high precision (>0.8) across clinically relevant recall levels, with declining precision at higher recall, reflecting the expected trade-off between sensitivity and false-positive detections. The system operated with low processing latency, supporting feasibility for real-time clinical use. Sensitivity was prioritized to mitigate the clinical risk associated with false-negative findings.
Conclusions:
CystoAID is a real-time, AI-based decision support tool for cystoscopy that demonstrated high sensitivity and favorable precision in external validation. These findings support its potential role as an assistive technology in routine urologic practice. Prospective studies are warranted to evaluate clinical impact, workflow integration, and performance in detecting challenging lesion subtypes, including flat lesions and carcinoma in situ.
