Machine Learning Models for Predicting Stone-Free Status after Shockwave Lithotripsy: A Systematic Review and...

Area of Science:

• Urology
• Medical Informatics
• Artificial Intelligence

Background:

• Shockwave Lithotripsy (SWL) is a common treatment for kidney stones.
• Predicting stone-free rates after SWL is crucial for patient management.
• Machine learning (ML) offers potential for improving predictive accuracy.

Purpose of the Study:

• To systematically review and meta-analyze the efficacy of ML techniques in predicting stone-free rates post-SWL.
• To compare the performance of ML algorithms against traditional prediction methods.

Main Methods:

• Systematic review and meta-analysis of eight studies involving 3264 patients.
• Inclusion of studies utilizing decision trees, Artificial Neural Networks (ANN), and hybrid ML approaches.
• Analysis of performance metrics including true positive rate, false positive rate, and Receiver Operator Characteristic (ROC) curves.

Main Results:

• Machine learning models demonstrated a summary true positive rate of 79% and a false positive rate of 14%.
• The overall Receiver Operator Characteristic (ROC) area was 0.90, indicating high discriminative ability.
• ML algorithms performed comparably to or better than standard prediction methods.

Conclusions:

• Machine learning techniques show significant promise for predicting stone-free rates following SWL.
• Current evidence suggests ML is at least as effective as conventional approaches.
• Prospective studies are recommended to validate and facilitate the routine clinical integration of ML in SWL practice.