1. Machine Learning Models for Predicting Stone-Free Status after Shockwave Lithotripsy: A Systematic Review and Meta-Analysis
- Author
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Robert Geraghty, Matthew Pugh, Bm Zeeshan Hameed, Bhaskar K. Somani, Patrick Rice, and Milap Shah
- Subjects
Urology ,Stone free ,030232 urology & nephrology ,MEDLINE ,Machine learning ,computer.software_genre ,Machine Learning ,Kidney Calculi ,03 medical and health sciences ,0302 clinical medicine ,Lithotripsy ,Humans ,Medicine ,Shockwave lithotripsy ,Artificial neural network ,Receiver operating characteristic ,business.industry ,Remission Induction ,Models, Theoretical ,Prognosis ,Clinical Practice ,030220 oncology & carcinogenesis ,Meta-analysis ,False positive rate ,Artificial intelligence ,business ,computer - Abstract
We performed a systematic review and meta-analysis to investigate the use of machine learning techniques for predicting stone-free rates following Shockwave Lithotripsy (SWL). Eight papers (3264 patients) were included. Two studies used decision-tree approaches, five studies utilised Artificial Neural Networks (ANN), and one study combined a variety of approaches. The summary true positive rate was 79%, summary false positive rate was 14%, and Receiver Operator Characteristic (ROC) was 0.90 for machine learning approaches. Machine learning algorithms were at least as good as standard approaches. Further prospective evidence is needed to routinely apply machine learning algorithms in clinical practice.
- Published
- 2021
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