The application of Machine learning in predicting the outcomes of minimally invasive treatments for uterine Fibroids: A systematic review and meta-analysis.
What's New
Machine learning models integrating radiomics and clinical data demonstrated moderate accuracy in predicting outcomes of minimally invasive uterine fibroid treatments.
Detailed Summary
A systematic review and meta-analysis of 14 studies evaluated machine learning models for predicting outcomes of minimally invasive uterine fibroid treatments (primarily HIFU and UAE). Meta-analysis of five HIFU-based radiomics studies showed pooled sensitivity of 75%, specificity of 76%, and an AUC of 0.82, though external validation was uncommon and risk of bias was frequently high.
Study Population
Women with uterine fibroids undergoing minimally invasive treatments such as HIFU or uterine artery embolization (14 studies)
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