ArticleNorthern clinics of Istanbul2025
Predictive value of machine learning-based T2-weighted MRI radiomics in the diagnosis of polycystic ovary syndrome.
Article in Northern clinics of Istanbul, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and deep learning for diagnosis of Polyendocrine Metabolic Ovarian Syndrome: systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
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7 authors.
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Abstract
objectiveThis study aims to explore the predictive performance of machine learning-based radiomic features extracted from T2-weighted magnetic resonance imaging (MRI) in differentiating between women with polycystic ovary syndrome (PCOS) and healthy counterparts.
methodsThe study included patients diagnosed with PCOS who had undergone pelvic MRI in the endocrine department between 2014 and 2022, along with an age-matched control group. The ovaries were manually segmented from T2-weighted images using the 3D Slicer software. Both first- and second-order features, including wavelet filters, were extracted from the images. Utilizing the Python 2.3 programming language and the Pycaret library, various machine learning algorithms were employed to identify highly correlated features. The optimal model was selected from the 15 algorithms assessed.
resultsThe study involved a total of 202 ovaries from 101 patients with PCOS (mean age 23±4 years) and 78 ovaries from the control group comprising 40 individuals (mean age 24±5 years). In the training set, the machine learning models displayed accuracy and area under the curve (AUC) values ranging from 72% to 83% and 0.50 to 0.81%, respectively. Notably, the Light Gradient Boosting Machine (LightGBM) model emerged as the most effective model among the various machine learning algorithms, exhibiting an AUC of 0.81 and an accuracy of 83%. When evaluated on the test set, the AUC, accuracy, recall, precision and F1 values of the LightGBM model were 0.80, 82%, 91%, 86%, 88%, respectively.
conclusionMachine learning-based T2-weighted MRI radiomics seems viable in differentiating between individuals with and without PCOS.
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