Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives—A Comprehensive Review
<b>Background</b>: Polycystic ovary syndrome (PCOS) is the most prevalent endocrinopathy in women of reproductive age, characterized by a broad spectrum of clinical, metabolic, and ultrasound findings. Over time, ultrasound has evolved into a cornerstone for diagnosing polycystic ovarian...
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2025-02-01
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| author | Stefano Di Michele Anna Maria Fulghesu Elena Pittui Martina Cordella Gilda Sicilia Giuseppina Mandurino Maurizio Nicola D’Alterio Salvatore Giovanni Vitale Stefano Angioni |
| author_facet | Stefano Di Michele Anna Maria Fulghesu Elena Pittui Martina Cordella Gilda Sicilia Giuseppina Mandurino Maurizio Nicola D’Alterio Salvatore Giovanni Vitale Stefano Angioni |
| author_sort | Stefano Di Michele |
| collection | DOAJ |
| description | <b>Background</b>: Polycystic ovary syndrome (PCOS) is the most prevalent endocrinopathy in women of reproductive age, characterized by a broad spectrum of clinical, metabolic, and ultrasound findings. Over time, ultrasound has evolved into a cornerstone for diagnosing polycystic ovarian morphology (PCOM), thanks to advances in probe technology, 3D imaging, and novel stromal markers. The recent incorporation of artificial intelligence (AI) further enhances diagnostic precision by reducing operator-related variability. <b>Methods</b>: We conducted a narrative review of English-language articles in PubMed and Embase using the keywords “PCOS”, “polycystic ovary syndrome”, “ultrasound”, “3D ultrasound”, and “ovarian stroma”. Studies on diagnostic criteria, imaging modalities, stromal assessment, and machine-learning algorithms were prioritized. Additional references were identified via citation screening. <b>Results</b>: Conventional 2D ultrasound remains essential in clinical practice, with follicle number per ovary (FNPO) and ovarian volume (OV) functioning as primary diagnostic criteria. However, sensitivity and specificity values vary significantly depending on probe frequency, cut-off thresholds (≥12, ≥20, or ≥25 follicles), and patient characteristics (e.g., adolescence, obesity). Three-dimensional (3D) ultrasound and Doppler techniques refine PCOS diagnosis by enabling automated follicle measurements, stromal/ovarian area ratio assessments, and evaluation of vascular indices correlating strongly with hyperandrogenism. Meanwhile, AI-driven ultrasound analysis has emerged as a promising tool for minimizing observer bias and validating advanced metrics (e.g., SA/OA ratio) that may overcome traditional limitations of stroma-based criteria. <b>Conclusions</b>: The continual evolution of ultrasound, encompassing higher probe frequencies, 3D enhancements, and now AI-assisted algorithms, has expanded our ability to characterize PCOM accurately. Nevertheless, challenges such as operator dependency and inter-observer variability persist despite standardized protocols; the integration of AI holds promise in further enhancing diagnostic accuracy. Future directions should focus on robust AI training datasets, multicenter validation, and age-/BMI-specific cut-offs to optimize the balance between sensitivity and specificity, ultimately facilitating earlier and more precise PCOS diagnoses. |
| format | Article |
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| institution | DOAJ |
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| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-e151371fc1ab4e259f910915a9490b9a2025-08-20T03:11:20ZengMDPI AGBiomedicines2227-90592025-02-0113245310.3390/biomedicines13020453Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives—A Comprehensive ReviewStefano Di Michele0Anna Maria Fulghesu1Elena Pittui2Martina Cordella3Gilda Sicilia4Giuseppina Mandurino5Maurizio Nicola D’Alterio6Salvatore Giovanni Vitale7Stefano Angioni8Division of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, SS554, 4, Monserrato, 09042 Cagliari, ItalyDivision of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, SS554, 4, Monserrato, 09042 Cagliari, ItalyDivision of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, SS554, 4, Monserrato, 09042 Cagliari, ItalyDivision of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, SS554, 4, Monserrato, 09042 Cagliari, ItalyDivision of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, SS554, 4, Monserrato, 09042 Cagliari, ItalyDivision of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, SS554, 4, Monserrato, 09042 Cagliari, ItalyDivision of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, SS554, 4, Monserrato, 09042 Cagliari, ItalyDivision of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, SS554, 4, Monserrato, 09042 Cagliari, ItalyDivision of Gynecology and Obstetrics, Department of Surgical Sciences, University of Cagliari, SS554, 4, Monserrato, 09042 Cagliari, Italy<b>Background</b>: Polycystic ovary syndrome (PCOS) is the most prevalent endocrinopathy in women of reproductive age, characterized by a broad spectrum of clinical, metabolic, and ultrasound findings. Over time, ultrasound has evolved into a cornerstone for diagnosing polycystic ovarian morphology (PCOM), thanks to advances in probe technology, 3D imaging, and novel stromal markers. The recent incorporation of artificial intelligence (AI) further enhances diagnostic precision by reducing operator-related variability. <b>Methods</b>: We conducted a narrative review of English-language articles in PubMed and Embase using the keywords “PCOS”, “polycystic ovary syndrome”, “ultrasound”, “3D ultrasound”, and “ovarian stroma”. Studies on diagnostic criteria, imaging modalities, stromal assessment, and machine-learning algorithms were prioritized. Additional references were identified via citation screening. <b>Results</b>: Conventional 2D ultrasound remains essential in clinical practice, with follicle number per ovary (FNPO) and ovarian volume (OV) functioning as primary diagnostic criteria. However, sensitivity and specificity values vary significantly depending on probe frequency, cut-off thresholds (≥12, ≥20, or ≥25 follicles), and patient characteristics (e.g., adolescence, obesity). Three-dimensional (3D) ultrasound and Doppler techniques refine PCOS diagnosis by enabling automated follicle measurements, stromal/ovarian area ratio assessments, and evaluation of vascular indices correlating strongly with hyperandrogenism. Meanwhile, AI-driven ultrasound analysis has emerged as a promising tool for minimizing observer bias and validating advanced metrics (e.g., SA/OA ratio) that may overcome traditional limitations of stroma-based criteria. <b>Conclusions</b>: The continual evolution of ultrasound, encompassing higher probe frequencies, 3D enhancements, and now AI-assisted algorithms, has expanded our ability to characterize PCOM accurately. Nevertheless, challenges such as operator dependency and inter-observer variability persist despite standardized protocols; the integration of AI holds promise in further enhancing diagnostic accuracy. Future directions should focus on robust AI training datasets, multicenter validation, and age-/BMI-specific cut-offs to optimize the balance between sensitivity and specificity, ultimately facilitating earlier and more precise PCOS diagnoses.https://www.mdpi.com/2227-9059/13/2/453PCOSPCOM3D ultrasoundovarian stromamachine learningsensitivity |
| spellingShingle | Stefano Di Michele Anna Maria Fulghesu Elena Pittui Martina Cordella Gilda Sicilia Giuseppina Mandurino Maurizio Nicola D’Alterio Salvatore Giovanni Vitale Stefano Angioni Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives—A Comprehensive Review Biomedicines PCOS PCOM 3D ultrasound ovarian stroma machine learning sensitivity |
| title | Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives—A Comprehensive Review |
| title_full | Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives—A Comprehensive Review |
| title_fullStr | Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives—A Comprehensive Review |
| title_full_unstemmed | Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives—A Comprehensive Review |
| title_short | Ultrasound Assessment in Polycystic Ovary Syndrome Diagnosis: From Origins to Future Perspectives—A Comprehensive Review |
| title_sort | ultrasound assessment in polycystic ovary syndrome diagnosis from origins to future perspectives a comprehensive review |
| topic | PCOS PCOM 3D ultrasound ovarian stroma machine learning sensitivity |
| url | https://www.mdpi.com/2227-9059/13/2/453 |
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