New AI system helps in rapid diagnosis of urolithiasis in CT with high precision: Study

A novel artificial intelligence (AI) system was created by Jin kim and team to identify kidney stones in CT (computed tomography) scans with speed and accuracy, the findings of this work were published in the recent issue of European Urology Focus journal.

A prominent cause of acute renal colic is urolithiasis which has been more widespread in the recent years. The frequency of emergency department (ER) visits for renal colic caused by urolithiasis is correlated with this growing trend. It is crucial to diagnose urolithiasis as soon as possible to avoid major side effects including urosepsis and hydronephrotic kidney damage.

This study aimed to create an artificial intelligence system that would use sophisticated deep learning to detect urolithiasis in computed tomography pictures. This system would be able to calculate the properties of the stone like volume and density in real time, which is crucial for making treatment decisions. In ER circumstances, the system's performance was compared to that of urologists.

The data set included axial CT scans for patients who had urolithiasis surgery between August 2022 and July 2023. The data set was split into three categories as training (70%), internal validation (10%) and testing (20%). Labelimg for ground-truth data was used to annotate stones by two urologists and an AI specialist. The RTX 4900 graphics processing unit (GPU) acceleration was applied to the YOLOv4 architecture during training. 100 individuals with probable urolithiasis had their CT pictures examined for external validation.