Can I read Machine Learning for Ureteral Stone Success on EtoBox?

Machine Learning for Ureteral Stone Success by Mario Siswanto is a document available to read on EtoBox.

What is Machine Learning for Ureteral Stone Success about?

This study developed and validated a machine learning-based decision support model to predict treatment success after single session shock wave lithotripsy for ureteral stones. The model achieved over 92% accuracy and identified key factors influencing treatment outcomes, including stone volume, length, and density. The findings suggest that machine learning can enhance clinical decision-making in urology by accurately predicting stone-free status post-treatment.

Author

Mario Siswanto

Language

EN