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

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 Neur...

Full description

Saved in:
Bibliographic Details
Published inUrology (Ridgewood, N.J.) Vol. 156; pp. 16 - 22
Main Authors Rice, Patrick, Pugh, Matthew, Geraghty, Rob, Hameed, BM Zeeshan, Shah, Milap, Somani, Bhaskar K
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 01.10.2021
Subjects
Humans
Kidney Calculi - therapy
Lithotripsy
Machine Learning
Models, Theoretical
Prognosis
Remission Induction
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Undefined-3
ISSN:0090-4295
1527-9995
1527-9995
DOI:10.1016/j.urology.2021.04.006