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...
OBJECTIVE: The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL).
The absence of predictive markers for kidney stone recurrence poses a challenge for the clinical management of stone disease. The unpredictability of stone events is also a significant limitation for clinical trials, where many patients must be enro...
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 diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Uti...
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...
Elevated levels of uric acid (UA) in the body may not only lead to the formation of stones but also increase the risk of developing chronic kidney disease (CKD). This study presents a biosensor for detecting UA concentration in stones and a deep lear...