Assessment and prediction models for the quantitative and qualitative reserve of the ovary using machine learning

Assessment and prediction models for the quantitative and qualitative reserve of the ovary using machine learning

Abstract Background The age-related decline of fertility is caused by a reduction of the ovarian reserve, which is represented by the number and quality of oocytes in the ovaries. Anti-Müllerian hormone (AMH) is considered one of the most useful markers of the quantity of the ovarian reserve; howeve...

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Main Authors: Hiroshi Koike, Miyuki Harada, Kaname Yoshida, Katsuhiko Noda, Chihiro Tsuchida, Toshihiro Fujiwara, Akari Kusamoto, Zixin Xu, Tsurugi Tanaka, Nanoka Sakaguchi, Chisato Kunitomi, Nozomi Takahashi, Yoko Urata, Kenbun Sone, Osamu Wada-Hiraike, Yasushi Hirota, Yutaka Osuga
Format: Article
Language:English
Published: BMC 2025-07-01
Series:Journal of Ovarian Research
Subjects:
Ovary
Ovarian reserve
Oocyte
Machine learning
Artificial intelligence
Assisted reproductive technology
Online Access:https://doi.org/10.1186/s13048-025-01732-0
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https://doi.org/10.1186/s13048-025-01732-0

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