Introduction: Flexible ureteroscopy (fURS) is a one-person surgical technique, limiting trainees’ ability to practice intra-operatively. Although well-suited for simulation training, few existing fURS simulators reproduce accurate, complex renal collecting system anatomies. We developed an anatomically accurate fURS simulator using 3D reconstructions of CT urograms and 3D printing technology to address this need. Methods: Patient-specific CT urograms were used to create 3D reconstructions of the renal collecting system using SlicerTM. 3D models were modified using BlenderTM. Hollow elastomer kidney models were created using an Objet 3DTM printer. To test and evaluate the new fURS simulator, 25 volunteers were recruited (5 novices, 13 residents, 7 urologists). Participants were asked to explore the model with fURS and were evaluated on their ability to deduce its 3D anatomy, their ability to navigate to prespecified calyces, and their time to task completion. Furthermore, participants were asked to compare the anatomical model to existing fURS benchtop models (Cook MedicalTM and Limb and ThingsTM) on several criteria, including internal visualization; tactile feedback; and overall functional and teaching fidelity, in a survey. Results: We were able to create a fURS simulator that accurately replicates anatomically complex renal collecting systems. In exploring the model, we noted that unlike staff urologists, novices and residents often completely missed lower pole calyces. A survey comparison between our simulator and comparable benchtop simulators revealed consistently better ratings of our simulator on all criteria (p < 0.05). Conclusion: We were able to successfully create an anatomically accurate fURS simulator that provides a more realistic scoping experience. Preliminary testing revealed that trainees will benefit from this simulator particularly with respect to learning how to navigate challenging collecting systems.

Background/Objectives: Bladder cancer, the most common and heterogeneous malignancy of the urinary tract, presents with diverse types and treatment options, making comprehensive patient education essential. As large language models (LLMs) emerge as a promising resource for disseminating medical information, their accuracy and validity compared to traditional methods remain under-explored. This study aims to evaluate the effectiveness of LLMs in educating the public about bladder cancer. Methods: Frequently asked questions regarding bladder cancer were sourced from reputable educational materials and assessed for accuracy, comprehensiveness, readability, and consistency by two independent board-certified urologists, with a third resolving any discrepancies. The study utilized a 3-point Likert scale for accuracy, a 5-point Likert scale for comprehensiveness, and the Flesch–Kincaid (FK) Grade Level and Flesch Reading Ease (FRE) scores to gauge readability. Results: ChatGPT-3.5, ChatGPT-4, and Gemini were evaluated on 12 general questions, 6 questions related to diagnosis, 28 concerning treatment, and 7 focused on prevention. Across all categories, the correct response rate was notably high, with ChatGPT-3.5 and ChatGPT-4 achieving 92.5%, compared to 86.3% for Gemini, with no significant difference in accuracy. However, there was a significant difference in comprehensiveness (p = 0.011) across the models. Overall, a significant difference in performance was observed among the LLMs (p < 0.001), with ChatGPT-4 providing the most college-level responses, though these were the most challenging to read. Conclusions: In conclusion, our study adds value to the applications of Artificial Intelligence (AI) in bladder cancer education, with notable insights into the accuracy, comprehensiveness, and stability of the three LLMs.

A 53-year-old man underwent a living donor kidney transplantation in the right iliac fossa 25 years prior to presentation. He had been noting an inguinal bugle that became more prominent as the day progressed, and it regressed at night. Upon further investigations, an ultrasound of the allograft revealed moderate-to-severe hydronephrosis. A computed tomography scan revealed herniation of the bladder and part of the transplanted ureter within the supravesical/direct inguinal space. Lichtenstein-like fashion of repair was performed, and the patient continues to enjoy satisfactory graft function with no recurrence. The case illustrates a rare hernia as a late complication of the kidney transplant incision leading to ureteric obstruction and a successful attempt at operative repair.

Background: Bladder cancer, the most common and heterogeneous malignancy of the urinary tract, presents with diverse types and treatment options, making comprehensive patient education essential. As Large Language Models (LLMs) emerge as a promising resource for disseminating medical information, their accuracy and validity compared to traditional methods remain under-explored. This study aims to evaluate the effectiveness of LLMs in educating the public about bladder cancer. Methods: Frequently asked questions regarding bladder cancer were sourced from reputable educational materials and assessed for accuracy, comprehensiveness, readability, and consistency by two independent board-certified urologists, with a third resolving any discrepancies. The study utilized a 3-point Likert scale for accuracy, a 5-point Likert scale for comprehensiveness, and the Flesch-Kincaid FK Grade Level and Flesch Reading Ease (FRE) scores to gauge readability. Results: ChatGPT-3.5, ChatGPT-4, and Gemini were evaluated on 12 general questions, 6 related to diagnosis, 28 concerning treatment, and 7 focused on prevention. Across all categories, the correct response rate was notably high, with ChatGPT-3.5 and ChatGPT-4 achieving 92.5%, compared to 86.3% for Gemini, with no significant difference in accuracy. However, there was a significant difference in comprehensiveness (p = 0.011) across the models. Overall, a significant difference in performance was observed among the LLMs (p < 0.001), with ChatGPT-4 providing the most college-level responses, though these were the most challenging to read. Conclusion: In conclusion, our study adds value to the applications of AI in bladder cancer education with notable insights on the accuracy, comprehensiveness, and stability of the three LLMs.