Objectives: This study aims to develop and evaluate an AI-assisted system for detecting
urological pathologies using cystoscopy images.
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