To optimize thulium fiber laser (TFL) settings for effective stone fragmentation although minimizing thermal injury in confined ureteral spaces using a three-dimensional ureter model. A hydrogel-based ureter model was maintained at 37.2 ± 0.5°C, wi...
With the rapid advancement of artificial intelligence in health care, large language models (LLMs) demonstrate increasing potential in medical applications. However, their performance in specialized oncology remains limited. This study evaluates the...
Diagnosing ureteral stones with low-dose CT in patients with metal hardware can be challenging because of image noise. The purpose of this study was to compare ureteral stone detection and image quality of low-dose and conventional CT scans with and...
In adult patients with ureteropelvic junction obstruction (UPJO), little data exist on predicting pyeloplasty outcome, and there is no unified definition of pyeloplasty success. As such, defining pyeloplasty success retrospectively is particularly v...
To evaluate and compare the quality and comprehensibility of answers produced by five distinct artificial intelligence (AI) chatbots-GPT-4, Claude, Mistral, Google PaLM, and Grok-in response to the most frequently searched questions about kidney sto...
We aimed to develop machine learning (ML) algorithms for the automated prediction of postoperative ureteroscopy outcomes for pediatric kidney stones based on preoperative characteristics. Data from pediatric patients who underwent ureteroscopy for ...
Kidney stones are common and morbid conditions in the general population with a rising incidence globally. Previous studies show substantial limitations of online sources of information regarding prevention and treatment. The objective of this study...
The multidisciplinary nature of artificial intelligence (AI) has allowed for rapid growth of its application in medical imaging. Artificial intelligence algorithms can augment various imaging modalities, such as X-rays, CT, and MRI, to improve image ...
This research presents our application of artificial intelligence (AI) in predicting urolithiasis risk. Previous applications, including AI for stone disease, have focused on stone composition and aiding diagnostic imaging. AI applications centered a...