Evaluating the effectiveness of AI-powered UrologiQ's in accurately measuring kidney stone volume in urolithiasis patients

Abstract

Kidney stones and urolithiasis are kidney diseases that have a significant impact on health and well-being, and their incidence is increasing annually owing to factors such as age, sex, ethnicity, and geographical location. Accurate identification and volume measurement of kidney stones are critical for determining the appropriate surgical approach, as timely and precise treatment is essential to prevent complications and ensure successful outcomes. Larger stones often require more invasive procedures, and precise volume measurements are essential for effective surgical planning and patient outcomes. This study aimed to compare the ability of artificial intelligence (AI) to detect and measure kidney stone volume via CT-KUB images. CT KUB imaging data were analyzed to determine the effectiveness of AI in identifying the volume of kidney stones. The results were compared with measurements taken by radiologists. Compared with radiologists, the AI had greater accuracy, efficiency, and consistency in measuring kidney stone volume. The AI calculates the volume of kidney stones with an average difference of 80% compared with the volumes calculated by radiologists, highlighting a significant discrepancy that is critical for accurate surgical planning. The results suggest that artificial intelligence (AI) outperforms radiologists' manual calculations in measuring kidney stone volume. By integrating AI with kidney stone detection and treatment, there is potential for greater diagnostic precision and treatment effectiveness, which could ultimately improve patient outcomes.

Keywords: Artificial intelligence; CT-KUB imaging; Kidney stone volume measurement; Percutaneous nephrolithotomy (PCNL); Radiologist comparison; Retrograde intrarenal surgery (RIRS); Surgical planning; Urolithiasis.