Evidence map›Paper›PMID 41868592›Full record

ArticleO&G open2026

Diagnosis of Polycystic Ovary Syndrome With Predictive Modeling of Select Clinical Features.

Adam T Evans, Eeshaan Rehani, Bailey Smith, Melody D Hong, Zoe Lewin, Karina Hiroshige, Steven D Spandorfer, Iman Hajirasouliha, Marla E Lujan, Kathleen M Hoeger

5 registry-linked trialsAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in O&G open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 5 registered trials, which are not on this map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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.

NCT01859663 completednot on this map

Baseline Assessment of Dietary Intake and Physical Activity in Women With Polycystic Ovary Syndrome (PCOS)

TypeobservationalSponsorCornell UniversityRan2013 to 2022Enrolled126ConditionsPolycystic Ovary Syndrome, Dietary Habits, Physical Activity
NCT01927432 completednot on this map

Ultrasound Characterization of Ovarian Follicle Dynamics in Women With Amenorrhea

TypeobservationalSponsorCornell UniversityRan2011 to 2021Enrolled73ConditionsOligomenorrhea, Amenorrhea, Polycystic Ovary Syndrome (PCOS)
NCT01927471 completednot on this map

Nutritional and Metabolic Correlates of Ovarian Morphology in Women With Polycystic Ovary Syndrome

TypeobservationalSponsorCornell UniversityRan2011 to 2019Enrolled129ConditionsPolycystic Ovary Syndrome (PCOS), Menstrual Irregularity
NCT03306849 completednot on this map

Ultrasonographic Evaluation of Anovulatory Disorders in Lean and Overweight Women

TypeobservationalSponsorCornell UniversityRan2015 to 2023Enrolled59ConditionsMenstrual Cycle Abnormal, PCOS, Overweight, Obesity
NCT03547453 completednot on this map

Ovarian Ultrasonography for the Clinical Evaluation of Polycystic Ovary Syndrome

TypeobservationalSponsorCornell UniversityRan2018 to 2022Enrolled240ConditionsPolycystic Ovary Syndrome (PCOS), Menstrual Irregularity, Overweight and Obesity
3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Adam T EvansDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York; Division of Reproductive Endocrinology and Infertility, University Hospitals/Case Western Reserve University School of Medicine, Beachwood, Ohio; Department of Physiology and Biophysics, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York; and Meinig School of Biomedical Engineering and Division of Nutritional Sciences, Cornell University, Ithaca, New York.
Eeshaan RehaniDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York; Division of Reproductive Endocrinology and Infertility, University Hospitals/Case Western Reserve University School of Medicine, Beachwood, Ohio; Department of Physiology and Biophysics, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York; and Meinig School of Biomedical Engineering and Division of Nutritional Sciences, Cornell University, Ithaca, New York.
Bailey SmithDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York; Division of Reproductive Endocrinology and Infertility, University Hospitals/Case Western Reserve University School of Medicine, Beachwood, Ohio; Department of Physiology and Biophysics, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York; and Meinig School of Biomedical Engineering and Division of Nutritional Sciences, Cornell University, Ithaca, New York.
Melody D HongDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York; Division of Reproductive Endocrinology and Infertility, University Hospitals/Case Western Reserve University School of Medicine, Beachwood, Ohio; Department of Physiology and Biophysics, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York; and Meinig School of Biomedical Engineering and Division of Nutritional Sciences, Cornell University, Ithaca, New York.
Zoe LewinDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York; Division of Reproductive Endocrinology and Infertility, University Hospitals/Case Western Reserve University School of Medicine, Beachwood, Ohio; Department of Physiology and Biophysics, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York; and Meinig School of Biomedical Engineering and Division of Nutritional Sciences, Cornell University, Ithaca, New York.
Karina HiroshigeDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York; Division of Reproductive Endocrinology and Infertility, University Hospitals/Case Western Reserve University School of Medicine, Beachwood, Ohio; Department of Physiology and Biophysics, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York; and Meinig School of Biomedical Engineering and Division of Nutritional Sciences, Cornell University, Ithaca, New York.
Steven D SpandorferDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York; Division of Reproductive Endocrinology and Infertility, University Hospitals/Case Western Reserve University School of Medicine, Beachwood, Ohio; Department of Physiology and Biophysics, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York; and Meinig School of Biomedical Engineering and Division of Nutritional Sciences, Cornell University, Ithaca, New York.
Iman HajirasoulihaDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York; Division of Reproductive Endocrinology and Infertility, University Hospitals/Case Western Reserve University School of Medicine, Beachwood, Ohio; Department of Physiology and Biophysics, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York; and Meinig School of Biomedical Engineering and Division of Nutritional Sciences, Cornell University, Ithaca, New York.
Marla E LujanDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York; Division of Reproductive Endocrinology and Infertility, University Hospitals/Case Western Reserve University School of Medicine, Beachwood, Ohio; Department of Physiology and Biophysics, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York; and Meinig School of Biomedical Engineering and Division of Nutritional Sciences, Cornell University, Ithaca, New York.
Kathleen M HoegerDepartment of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, New York; Division of Reproductive Endocrinology and Infertility, University Hospitals/Case Western Reserve University School of Medicine, Beachwood, Ohio; Department of Physiology and Biophysics, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York; and Meinig School of Biomedical Engineering and Division of Nutritional Sciences, Cornell University, Ithaca, New York.

Funding

Artificial intelligence for the automated detection of polycystic ovary syndrome on ultrasonographyR21HD116139 · NICHD · CORNELL UNIVERSITY · PI KATHLEEN M HOEGER, Iman Hajirasouliha · 2025 to 2026
$453k
NICHD NIH HHS R21 HD116139
6 · The paper itself

Abstract

objectiveTo determine whether a limited set of ultrasonographic, biochemical, and clinical features are sufficient to accurately predict polycystic ovary syndrome (PCOS) diagnosis.

methodsTransvaginal ultrasound images and available clinical data for participants with PCOS (n=101) and controls (n=50) were used for this multicenter, retrospective pilot study. Diagnosis of PCOS was defined by the 2023 International Evidence-Based Guideline. Controls had no diagnostic features of PCOS. Differences in demographic (age, body mass index [BMI]), ultrasonographic (ovarian volume, follicle number per ovary, follicle number per single cross section), biochemical (sex hormone binding globulin, total testosterone, free androgen index, bioavailable testosterone), and clinical (follicle-stimulating hormone, luteinizing hormone, estradiol, anti-müllerian hormone (AMH), age at menarche, minimum self-reported menstrual cycle length in the past year, maximum self-reported menstrual cycle length in the past year, Ferriman-Gallwey hirsutism score) features between groups were assessed with Mann-Whitney

resultsAnti-müllerian hormone alone predicted PCOS diagnosis with relatively good diagnostic accuracy (AUROC 0.884, F1 score 0.807). Integration of AMH and ovarian volume improved model performance (AUROC 0.906, F1 score 0.811). Integration of all ultrasonographic, biochemical, and clinical features yielded a high-performing model with excellent diagnostic accuracy for PCOS (AUROC 0.991, F1 score 0.811). Refinement of the model to a limited set of readily obtained variables, including AMH, ovarian volume, hirsutism score, and maximum self-reported menstrual cycle length, yielded a model with strong performance (AUROC 0.982, F1 score 0.805).

conclusionA minimum combination of ovarian volume with AMH and examination/history-derived end points can accurately predict PCOS diagnosis with performance comparable to that of a combination of all ultrasonographic, biochemical, and clinical features. This may streamline diagnostic workflows, thereby reducing clinical burden. PARENT PROTOCOL CLINICAL

trial registrationClinicalTrials.gov, NCT01859663, NCT01927471, NCT03306849, NCT01927432, NCT03547453.

Identifiers

PMID41868592
PMCPMC13002152

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Registered trials

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.