AIMC Topic: Urolithiasis

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Systematic evaluation of deepseek in urolithiasis: from medical knowledge to clinical decision support.

World journal of urology
BACKGROUND: Large language models (LLMs), such as ChatGPT, have demonstrated promising potential in medical knowledge retrieval and clinical decision support. DeepSeek, a China-developed model released in 2025, has been proposed as a medical AI tool,...

Noninvasive CT radiomics-clinical model accurately classifies anhydrous uric acid stones: a multicenter study.

World journal of urology
BACKGROUND: Urolithiasis, particularly anhydrous uric acid stones (AUAs), imposes significant clinical and economic burdens. Accurate preoperative differentiation of AUAs from other stone types remains challenging, yet essential for personalized pati...

Intraoperative use of artificial intelligence (AI) during endoscopic lithotripsy: a systematic review from EAU endourology.

World journal of urology
INTRODUCTION: The current systematic review aims to summarize the existing data on intraoperative use of artificial intelligence (AI) during endoscopic lithotripsy in order to assess which particular applications are feasible and have prospects of wi...

Artificial Intelligence and Machine Learning for Stone Management.

The Urologic clinics of North America
Stone disease management is continuously evolving through the introduction of novel tools and technologies. Artificial intelligence and machine learning (ML) promise a new technological frontier for the enhancement of urolithiasis diagnosis, treatmen...

Development and evaluation of USCnet: an AI-based model for preoperative prediction of infectious and non-infectious urolithiasis.

World journal of urology
BACKGROUND: Urolithiasis, a prevalent condition characterized by a high rate of incidence and recurrence, necessitates accurate preoperative diagnostic methods to determine stone composition for effective clinical management. Current diagnostic pract...

A Novel Deep Learning-based Artificial Intelligence System for Interpreting Urolithiasis in Computed Tomography.

European urology focus
BACKGROUND AND OBJECTIVE: Our aim was to develop an artificial intelligence (AI) system for detection of urolithiasis in computed tomography (CT) images using advanced deep learning capable of real-time calculation of stone parameters such as volume ...

Artificial Intelligence in Urology: Application of a Machine Learning Model to Predict the Risk of Urolithiasis in a General Population.

Journal of endourology
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...

Comparative analysis of artificial intelligence chatbot recommendations for urolithiasis management: A study of EAU guideline compliance.

The French journal of urology
OBJECTIVES: Artificial intelligence (AI) applications are increasingly being utilized by both patients and physicians for accessing medical information. This study focused on the urolithiasis section (pertaining to kidney and ureteral stones) of the ...

Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR).

Abdominal radiology (New York)
PURPOSE: Urolithiasis is a chronic condition that leads to repeated CT scans throughout the patient's life. The goal was to assess the diagnostic performance and image quality of submillisievert abdominopelvic computed tomography (CT) using deep lear...

Dose Optimization Using a Deep Learning Tool in Various CT Protocols for Urolithiasis: A Physical Human Phantom Study.

Medicina (Kaunas, Lithuania)
We attempted to determine the optimal radiation dose to maintain image quality using a deep learning application in a physical human phantom. Three 5 × 5 × 5 mm uric acid stones were placed in a physical human phantom in various locations. Three tu...