UrologiQ: AI-based accurate detection, measurement and reporting of stones in CT-KUB scans

Abstract

Kidney stone disease is becoming increasingly common worldwide, with its prevalence increasing annually across all age groups, races, and geographic regions. This sharp increase may be due to significant changes in dietary habits. Early and accurate detection of kidney stones is crucial for timely intervention and prevention of complications. This article discusses the role of artificial intelligence (AI) in detecting kidney stones and managing surgical treatments. Recent advances in AI techniques have introduced new tools that improve the diagnosis and analysis of medical images. AI can use CT-KUB image data to accurately detect the location of kidney stones and measure their size more efficiently than manual methods. AI-based detection methods ensure greater precision and consistency in stone identification and measurement. These improvements can help doctors plan treatments more effectively, resulting in a higher success rate for patients. Integrating AI into kidney stone detection and analysis significantly improves treatment planning and patient management, leading to better patient outcomes and overall quality of healthcare.

Keywords: Nephrolithiasis; Non-contrast computed tomography scan of the kidneys, ureter, and bladder (NCCT-KUB); Percutaneous nephrolithotomy (PCNL); Retrograde intrarenal surgery (RIRS).