Diagnostic accuracy of machine learning for endometriosis: a systematic review and meta-analysis

Key Points

• Diagnostic accuracy of machine learning techniques for endometriosis is critically assessed, highlighting their potential efficacy.
• Overall performance metrics indicate variability, with sensitivity reported as high as 85%, suggesting promise in early detection.
• Systematic review and meta-analysis summarize findings from multiple studies, addressing the diagnostic challenges of endometriosis.
• Results underscore the need for standardized approaches and further validation in diverse clinical settings.

Abstract