Background/Objectives: Cystoscopy is necessary for diagnosing bladder cancer, but it has limitations in identifying ambiguous lesions, such as carcinoma in situ (CIS), which leads to a high recurrence rate of bladder cancer. With the significant advancements in deep learning in the medical field, several studies have explored its application in cystoscopy. This study aimed to utilize the VGG19 and Deeplab v3+ deep learning models to classify and segment cystoscope images, respectively. Methods: We classified cystoscope images obtained from 772 patients based on morphology (normal, papillary, flat, mixed) and biopsy results (normal, Ta, T1, T2, CIS, etc.). Experienced urologists annotated and labeled the lesion areas and image categories. The classification model for bladder cancer lesion, annotated with pathological results, was developed using VGG19 with an additional fully connected layer, utilizing sparse categorical cross-entropy as the loss function. The Deeplab v3+ model was used for segmenting each morphological type of bladder cancer in the cystoscope images, employing the dice coefficient loss function. The classification model was evaluated using validation accuracy and correlation with biopsy results, while the segmentation model was assessed using the Intersection over Union (IoU) combined with binary accuracy. Results: The dataset was split into training and validation sets with a 4:1 ratio. The VGG19 classification model achieved an accuracy score of 0.912. The Deeplab v3+ segmentation model achieved an IoU of 0.833 and a binary accuracy of 0.951. Visual analysis revealed a high similarity between the lesions identified by Deeplab v3+ and those labeled by experts. Conclusions: In this study, we applied two deep learning models using well-annotated datasets of cystoscopic images. Both VGG19 and Deeplab v3+ demonstrated high performance in classification and segmentation, respectively. These models can serve as valuable tools for bladder cancer research and may aid in the diagnosis of bladder cancer.

During rigid ureteroscopic lithotripsy, it is often encountered that the ureter is difficult to access. Attempts to advance the ureteroscope make the surgery more difficult. This study evaluated the preoperative predictive factors associated with difficult ureteral access (difficult ureter (DU)) during URS and assessed if clinical outcomes differed according to the degree of DU. This study identified 217 patients who underwent rigid ureteroscopic (URS) lithotripsy for the management of ureter stones between June 2017 and July 2021 in a tertiary hospital in Korea. In this group, preoperative factors were identified using univariate and multiple logistic regression analyses that could predict the degree of DU. Additionally, we also evaluated differences in treatment outcomes depending on the degree of DU. In 50 URS cases (22.0%), ureteral access using a ureteroscope was difficult. In the univariate and multivariate analyses, the degree of hydronephrosis was associated with the degree of DU. Treatment outcomes, extended operation times, low stone-free rate, postoperative pain, and secondary treatment were also significantly associated with the degree of DU. Clinicians can counsel patients with a lesser degree of hydronephrosis and approach their management accordingly.