Materials and Methods: A dataset comprising 500 pathological and 500 healthy cystoscopy images was collected from the urology clinic of training and research hospital. Images were obtained using three different endovision systems (Karl Storz [Germany], Stryker [USA], Richard Wolf [Germany]). The dataset was preprocessed, augmented, and used to train a Convolutional Neural Network (CNN) model to classify images as either normal or pathological. The model"s performance was evaluated on a test set comprising 100 pathological and 100 healthy images, using metrics such as accuracy, sensitivity, specificity, and F1-score. Statistical analyses were performed using IBM SPSS version 25.0, with a p-value of <0.05 considered significant.

Results: The model achieved a sensitivity of 94% for detecting pathological cases and a specificity of 58% for correctly identifying healthy cases. For pathological images, precision, recall, and F1-score were 0.69, 0.94, and 0.80, respectively, while for healthy images, these metrics were 0.91, 0.60, and 0.72. The overall accuracy of the model was recorded as 76%.

Conclusion: The AI-assisted cystoscopy image analysis system demonstrates high sensitivity in detecting urological pathologies but requires further improvements to enhance specificity. Future studies should focus on increasing dataset diversity and improving the model"s ability to distinguish between benign and malignant features. The integration of higher-quality images and advanced AI techniques holds great potential for enhancing the model"s success and improving diagnostic accuracy.