Automatic detection of urinary stones from non-contrast enhanced computed tomography images.

Urinary stones, one of the most common emergency conditions, traverse the ureter, urine flow is obstructed, resulting in hydronephrosis and severe pain. However, vessel wall calcifications or phleboliths are frequently observed in abdominal and pelvic regions and distinguishing them from urinary stones can be challenging. This study was performed to implement deep learning techniques, specifically utilizing the UROAID (UROlothiasis AssIsted Diagnosis system) model, to detect urinary stones within the urinary tract. Noncontrast abdominopelvic computed topographies (CT) performed on adult patients at the emergency departments of the two tertiary academic hospitals were collected. The ROI Extraction and KUB Segmentation algorithms were a modified version of Uro-UNETR. The 3D labelling map and 3D stone classification were individual outputs that were then merged with the results from the Urinary System Estimation module in the UROAID detection module. In total, the CT scans of 6659 patients were included in the study. An accuracy of 0.9585 and an F1 score of 0.9605 were achieved using an ensemble model alongside a stone classification module that we also proposed to further improve the performance. The detection rate of UROAID for stones by location was highest for stones in the kidney, with a rate of 99.0%, followed by the proximal ureter (99.1%), middle ureter (98.0%), distal ureter (96.4%), and urinary bladder (91.3%). This study designed UROAID, an ensemble model of a segmentation-based stone detection module and a stone classification module, to follow the process of a radiologist accurately diagnosing urinary stones.