Abstract
OBJECTIVES: Bladder cancer is one of the most common malignancies of the urinary system, and early diagnosis and treatment are crucial for improving patient prognosis. METHODS: In this study, we developed an artificial intelligence (AI) model for bladder cancer diagnosis using CT imaging data from our hospital and The Cancer Imaging Archive (TCIA). The model was trained and validated using a large dataset of CT images, and its diagnostic accuracy was assessed through various performance metrics. The AI model was constructed using deep learning techniques, which can automatically learn and extract features from CT images. Retrospective CT data from our hospital and TCIA were used to train the AI model. Performance metrics were evaluated, and the Grad-CAM results were reviewed by radiologists. RESULTS: The test-set accuracy exceeded 0.90, and Grad-CAM correctly highlighted tumors. CONCLUSIONS: The AI model offers accurate and transparent bladder-cancer detection on routine CT scans and merits prospective validation.