Machine learning and deep learning for diagnosis of endometriosis: systematic review and meta-analysis
- 1Chengdu University of Traditional Chinese Medicine , ,
- 2Department of Gynecology, Hospital of Chengdu University of Traditional Chinese Medicine , ,
Abstract
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.
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BibTeX
@article{guo2026machine,
title = {{Machine learning and deep learning for diagnosis of endometriosis: systematic review and meta-analysis}},
author = {Jiayi Guo and Xue Liu and Huiying Zhang and Shaobin Wei},
journal = {Frontiers in medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1907483},
url = {https://europepmc.org/article/PMC/PMC13716802},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine learning and deep learning for diagnosis of endometriosis: systematic review and meta-analysis
AU - Jiayi Guo
AU - Xue Liu
AU - Huiying Zhang
AU - Shaobin Wei
PY - 2026
DA - 2026/10/15
JO - Frontiers in medicine
DO - 10.3389/fmed.2026.1907483
UR - https://europepmc.org/article/PMC/PMC13716802
AB - 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.
SN - 2296-858X
ER -