The role of AI in Detection and Diagnosis of Kidney stone

Abstract

Background: Kidney stone disease (nephrolithiasis) is a prevalent and recurrent condition, affecting approximately 12–15% of the Indian population. Conventional diagnostic modalities such as computed tomography (CT), ultrasound (US), and X-ray imaging are widely used but have notable limitations, including radiation exposure, operator dependency, variability in interpretation, and reduced sensitivity for small or radiolucent stones. Objective: To evaluate the role of artificial intelligence in the detection and diagnosis of 
kidney stones across various imaging modalities and clinical applications. Methods: This narrative review evaluates recent studies on AI applications in nephrolithiasis, focusing on machine learning (ML) and deep learning (DL) techniques applied to imaging modalities (CT, US, X-ray), clinical prediction models, and advanced analytical methods such as spectroscopy and microscopy. Results: AI-based models significantly enhance diagnostic performance by enabling automated detection, segmentation, and characterization of kidney stones, achieving accuracies exceeding 90% in several studies. AI improves imaging interpretation across CT, US, and X-ray while reducing observer variability and enabling low-dose imaging protocols. Additionally, AI demonstrates 
strong predictive capabilities for clinical outcomes, including spontaneous stone passage, extracorporeal shock wave lithotripsy(ESWL) success, and endourological procedure outcomes, often outperforming traditional statistical models. AI also enables accurate identification of stone composition through imaging data and advanced analytical techniques, supporting personalized treatment strategies. Conclusion: Artificial intelligence represents a transformative advancement in kidney stone management by improving diagnostic accuracy, reducing variability, and facilitating personalized care. However, further large-scale validation and integration 
into clinical workflows are required to ensure widespread adoption and optimal patient outcomes.