Machine learning and deep learning for diagnosis of endometriosis: systematic review and meta-analysis
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Jiayi Guo1, Xue Liu1, Huiying Zhang1
1Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Objective:
To assess the diagnostic accuracy of machine learning (ML) and deep learning (DL) models for endometriosis.
Methods:
A systematic review and meta-analysis were conducted in accordance with PRISMA guidelines (PROSPERO: CRD420251231469). Databases were searched through November 24, 2025. Twenty-two eligible studies were included. Risk of bias was assessed using QUADAS-2. Pooled analysis was conducted in Stata 18.0.
Results:
Pooled sensitivity was 0.90 (95% CI 0.86-0.93), specificity 0.75 (95% CI 0.64-0.84), and SROC-AUC 0.92 (95% CI 0.89-0.94). A positive prediction increased disease probability from 10% to 29%, whereas a negative prediction reduced it to 1%. Multicenter design and DL were associated with lower sensitivity. Results were robust.
Conclusions:
ML/DL models demonstrate high diagnostic performance and are promising for non-invasive auxiliary diagnosis. Large multicenter prospective studies and multimodal models are needed to enable clinical translation.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251231469, identifier CRD420251231469.