From Stones to Signals: Harnessing Artificial Intelligence for Precision Diagnosis and Recurrence Prediction in Urolithiasis: A Comprehensive Review
DOI:
https://doi.org/10.46799/jhs.v7i5.2829Keywords:
artificial intelligence, urolithiasis, urinary stone disease, disease managementAbstract
Urolithiasis is a common urological disorder with increasing global prevalence and a high recurrence rate, creating significant clinical and economic burdens on healthcare systems worldwide. Advances in medical imaging, minimally invasive surgery, and artificial intelligence (AI) technologies have transformed the diagnosis and management of urinary tract stones. This review aimed to evaluate the role of AI and modern therapeutic innovations in improving diagnostic accuracy, treatment planning, and prediction of clinical outcomes in patients with urolithiasis. A comprehensive literature review was conducted using electronic databases, including PubMed, Scopus, ScienceDirect, SpringerLink, and Google Scholar. Relevant articles published between 2016 and 2026 were selected based on inclusion and exclusion criteria related to AI applications, imaging technologies, and minimally invasive interventions in urolithiasis management. The findings demonstrated that AI-based technologies, particularly machine learning and convolutional neural network algorithms, significantly improved the detection and classification of urinary stones through CT scan and ultrasound imaging. AI also contributed to individualized therapy planning, prediction of stone recurrence, and optimization of minimally invasive procedures such as ESWL, URS, PCNL, and RIRS. In addition, innovations in laser technologies, including Holmium:YAG and Thulium Fiber Laser, enhanced stone fragmentation efficiency and reduced complications. In conclusion, AI and technological advancements have substantial potential to support precision medicine and improve clinical outcomes in urolithiasis management, although further large-scale clinical validation remains necessary.
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