Value of ultrasonography parameters in diagnosing polycystic ovary syndrome
Abstract: Polycystic ovary syndrome (PCOS) is a common endocrinopathy among women of reproductive age associated with hyperandrogenism, oligo-amenorrhea, and infertility. Symptoms and their severity vary among the individuals. If the manifestation is mild, PCOS may remain undiagnosed. In more severe cases, it results in a spectrum of symptoms of metabolic syndrome, insulin resistance, and cardiovascular diseases. The diagnosis is established after a physical examination and evaluating the patient’s hormonal profile. In addition to these required methods, ultrasonographic assessment of the patient’s ovaries is another non-invasive, cheap, and time-saving tool, making the examination more profound and leading to the correct diagnosis. Specific ultrasonographic parameters are used to tell the healthy and polycystic ovaries apart: the ovarian volume (OV), ovarian follicle count, follicle distribution pattern, ovarian stromal echogenicity, and the resistance and pulsatility indices assessed using the Doppler function. This review evaluated the selected articles and ascertained the ultrasonographic parameters that accurately predict PCOS. This systematic review showed that the most valuable ultrasonographic parameters in diagnosing PCOS are the OV and follicle number per ovary.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Value of ultrasonography parameters in diagnosing polycystic ovary syndrome ; volume:17 ; number:1 ; year:2022 ; pages:1114-1122 ; extent:9
Open medicine ; 17, Heft 1 (2022), 1114-1122 (gesamt 9)
- Creator
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Gyliene, Augustina
Straksyte, Vestina
Zaboriene, Inga
- DOI
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10.1515/med-2022-0505
- URN
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urn:nbn:de:101:1-2022071515245302609948
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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2025-08-15T07:38:19+0200
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Gyliene, Augustina
- Straksyte, Vestina
- Zaboriene, Inga