Machine learning and deep learning for diagnosis of Polyendocrine Metabolic Ovarian Syndrome: systematic review and meta-analysis
Abstract
Background: Polyendocrine Metabolic Ovarian Syndrome (PMOS) is a prevalent endocrine disorder with a challenging, heterogeneous diagnosis. Machine learning (ML) and deep learning (DL) models show promise for automated diagnosis, but a quantitative synthesis of their accuracy is lacking.
Objective: To evaluate the diagnostic accuracy of ML/DL models for PMOS and identify factors influencing performance.
Methods: We searched MEDLINE, Web of Science, Embase, and Cochrane Library from inception to January 26, 2026. Studies developing or validating ML/DL models for PMOS diagnosis were included. Risk of bias was assessed using QUADAS-2. A random-effects model with the Hartung-Knapp-Sidik-Jonkman method was used to pool estimates. Hierarchical summary receiver operating characteristic curves were constructed. Heterogeneity was quantified (I 2), and meta-regression and subgroup analyses explored sources of heterogeneity.
Results: Fifty-six studies (60 datasets) were included. Pooled sensitivity was 0.92 (95% CI: 0.89-0.94; I2 = 93.6%) and specificity 0.94 (95% CI: 0.91-0.96; I 2 = 93.4%), with an HSROC area under the curve of 0.99 (95% CI: 0.97-0.99). Prediction intervals were wide (sensitivity: 50%-99%; specificity: 53%-100%). Egger's test suggested small-study effects (P P = 0.002) and modeling variables (ultrasound image vs. clinical parameters, coefficient=2.43, P = 0.007) as independent predictors of higher accuracy; model type was not significant after adjustment. Ultrasound…
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