Evidence map›Paper›PMID 40676609›Full record

ArticleJournal of ovarian research2025

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

Hiroshi Koike, Miyuki Harada, Kaname Yoshida, Katsuhiko Noda, Chihiro Tsuchida, Toshihiro Fujiwara, Akari Kusamoto, Zixin Xu, Tsurugi Tanaka, Nanoka Sakaguchi and 7 more

Abstract read
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In one paragraph

Article in Journal of ovarian research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Hiroshi KoikeDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Miyuki HaradaDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. haradam-tky@umin.ac.jp.
Kaname YoshidaSIOS Technology, Inc., 2-12-3, Minamiazabu, Minato-ku, Tokyo, 106-0047, Japan.
Katsuhiko NodaSIOS Technology, Inc., 2-12-3, Minamiazabu, Minato-ku, Tokyo, 106-0047, Japan.
Chihiro TsuchidaDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Toshihiro FujiwaraPhoenix ART Clinic, 3-12-18, Sendagaya, Shibuya-ku, Tokyo, 151-0051, Japan.
Akari KusamotoDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Zixin XuDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Tsurugi TanakaDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Nanoka SakaguchiDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Chisato KunitomiDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Nozomi TakahashiDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Yoko UrataDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Kenbun SoneDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Osamu Wada-HiraikeDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Yasushi HirotaDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Yutaka OsugaDepartment of Obstetrics and Gynecology, Faculty of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.

Funding

Japan Society for the Promotion of Science 21j12871Japan Society for the Promotion of Science 21k16808Japan Society for the Promotion of Science 22k09614Japan Society for the Promotion of Science 23k15803Japan Society for the Promotion of Science 23k19667Japan Society for the Promotion of Science 24k23506
6 · The paper itself

Abstract

backgroundThe 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; however, a more accurate prediction method is required. Furthermore, there is no clinically useful tool to assess the quality of the ovarian reserve and therefore a prediction tool is required. Our aim is to produce a model for prediction of the ovarian reserve that contributes to preconception care and precision medicine.

methodsThis study was a retrospective analysis of 442 patients undergoing assisted reproductive technology (ART) treatment in Japan from June 2021 to January 2023. Medical records and residual serum of patients undergoing oocyte retrieval were collected. Binary classification models predicting the ovarian reserve were created using machine learning methods developed with many collected feature values. The best-performing model among 15 examined models was selected based on its area under the receiver operating characteristic curve (AUC) and accuracy. To maximize performance, feature values used for model creation were narrowed down and extracted.

resultsThe best-performing model to assess the quantity of the ovarian reserve was the random forest model with an AUC of 0.9101. Five features were selected to create the model and consisted of data from only medical records. The best-performing model to assess the quality of the ovarian reserve was the random forest model, which had an AUC of 0.7983 and was created with 14 features, data from medical records and residual serum analysis.

conclusionOur models are more accurate than currently popular methods for predicting the ovarian reserve. Furthermore, they can assess the ovarian reserve using only information obtained from a medical interview and single blood sampling. Enabling easy measurement of the ovarian reserve with this model would allow a greater number of women to engage in preconception care and facilitate the delivery of personalized medical treatment for patients undergoing infertility therapy. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Machine LearningOvarian ReserveOvaryAdultAnti-Mullerian HormoneFemaleHumansRetrospective StudiesROC CurveAnti-Mullerian HormoneArtificial intelligenceAssisted reproductive technologyMachine learningOocyteOvarian reserveOvaryPreconception care

Identifiers

PMID40676609
PMCPMC12273052

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.