A Machine Learning Approach for Non-Invasive PCOS Diagnosis from Ultrasound and Clinical Features

    September 2025 in “ Scientific Reports ”
    Mehtap Agirsoy, Matthew A. Oehlschlaeger
    Studysummary This study found that using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.
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    Research cited in this study 8

    1. Polycystic Ovary Syndrome Nature Reviews Disease Primers · 2024
    2. Recommendations From The 2023 International Evidence-Based Guideline For The Assessment And Management Of Polycystic Ovary Syndrome Fertility and Sterility · 2023
    3. Recommendations From The 2023 International Evidence-Based Guideline For The Assessment And Management Of Polycystic Ovary Syndrome The Journal of Clinical Endocrinology and Metabolism · 2023
    4. PCOS in Adolescents—Ongoing Riddles in Diagnosis and Treatment Journal of Clinical Medicine · 2023
    5. Obesity and Polycystic Ovary Syndrome: Implications for Pathogenesis and Novel Management Strategies Clinical medicine insights · 2019
    6. The Androgen Excess and PCOS Society Criteria for the Polycystic Ovary Syndrome: The Complete Task Force Report Fertility and Sterility · 2008
    7. Relative Prevalence of Different Androgen Excess Disorders in 950 Women Referred Because of Clinical Hyperandrogenism The Journal of Clinical Endocrinology and Metabolism · 2006
    8. Health Care-Related Economic Burden of Polycystic Ovary Syndrome During the Reproductive Life Span The Journal of Clinical Endocrinology and Metabolism · 2005