Artificial Intelligence Detection of Bladder Tumors Under Endoscopy

1. Research Background Bladder cancer is the ninth most common malignancy worldwide, with an estimated 430,000 new cases diagnosed annually. The standard diagnosis and monitoring of bladder cancer rely on white light cystoscopy (WLC), with over 2 million cystoscopies performed annually in the United States and Europe. Due to the high recurrence rate of bladder cancer, frequent monitoring and intervention are necessary.

Early detection and complete resection of non-muscle invasive bladder cancer can reduce recurrence and progression. However, up to 40% of patients with multifocal disease do not achieve complete resection during the initial transurethral resection of bladder tumor (TURBT). Many papillary tumors and flat lesions are difficult to identify through WLC. There is an urgent need for cost-effective, non-invasive, and user-friendly adjunct imaging technologies to address the diagnostic deficiencies of WLC.

Recent advancements in deep learning-based automated image processing may provide new solutions to the limitations of cystoscopy. Convolutional neural networks (CNNs) possess the ability to learn complex relationships and integrate existing knowledge into models, showing potential applications across various fields, including bladder tumor diagnosis. We employed the HRNet algorithm, a convolutional neural network, for enhanced bladder tumor detection. 2. Research Objectives We aim to explore the potential application of AI in urological tumors by collecting cystoscopy videos from patients undergoing cystoscopy. These videos include bladder tumors will be annotated manually by urologists, then, the AI algorithm will be used to recognize the bladder tumors. 3. Research Methods This is a multicenter, retrospective, observational study. 4. Research Process 4.1 Patient Cohort Inclusion criteria: 1. The patients who had bladder tumor and received WLC or TURBT, and the full-length surgery video is available.

Exclusion criteria: 1. The video is too blurry to distinguish the normal bladder wall and bladder tumor. 2. Lack of the appearance of bladder tumor before resection. 3. Lack of informed consent.

Patient information in the videos will not be shown. Videos from the initially recruited 200 bladder tumor patients will be used for algorithm development. Videos from an additional 100 patients are used for algorithm validation.