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Cited by 91Ovarian function and disordersArtificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review

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Barrera, Francisco J., Brown, Ethan D.L., Rojo, Amanda, Obeso, Javier, Plata, Hiram, Lincango, Eddy P., Terry, Nancy, Rodríguez-Gutiérrez, René +2 more·2023·Frontiers in Endocrinology
DOI 10.3389/fendo.2023.1106625PMC PMC10542899
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Abstract

Introduction: Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS. Methods: We applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022. Results: 135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies). Conclusion: Artificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility. Systematic review registration: https://www.crd.york.ac.uk/prospero/ , identifier CRD42022295287.

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  • The majority of machine learning studies originated from India (n = 5), while three originated from the USA, and one from New Zealand.

    “The majority of these studies originated from India (n = 5) [63,64,65,66,88], while three originated from the USA (n = 3) [67,87,89], and one from New Zealand [76].”

    From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [87]

    The Results, under “Characteristics of the included studies” (page not available), report 31 included studies, with 29% conducted in India—about nine, not five. Table 1 also lists at least four USA studies, rather than three. The review therefore gives a different geographic distribution from the one attributed to it. (LLM, conf=0.85)

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  • Ultrasound images were used in 48% of the studies, and only a third (n = 10, 32%) used standardized PCOS diagnostic criteria as a reference to evaluate their results.

    “Ultrasound images were used in 48% of them, and only a third (n = 10, 32%) used standardized PCOS diagnostic criteria as a reference to evaluate their results [87].”

    From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [87]

    In Results, “Characteristics of the included studies” (page unavailable), the review reports that 48% of studies used ultrasound images and that ten (32%) used validated diagnostic criteria. It says those criteria were used to select the study population, not specifically as a reference to evaluate the studies’ results. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Nine included studies mention the use of machine learning.

    “Nine included studies mention the use of machine learning [63,64,65,66,67,76,87,88,89] (Supplementary File S3).”

    From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [87]

  • The utility of AI and machine learning in the diagnosis of PCOS using clinical, genetic, and proteomic data, and electronic health records as data sources was explored in a systematic review.

    “The utility of AI and machine learning in the diagnosis of PCOS using clinical, genetic, and proteomic data, and electronic health records as data sources was explored in a systematic review [87].”

    From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [87]

  • This systematic review included 31 studies, predominantly from India (29%) or China (16%), with sample sizes ranging from 9 to 2000 and a median age of 29 years.

    “This included 31 studies, which were predominantly from India (29%) or China (16%), with sample sizes ranging from 9 to 2000 and a median age of 29 years [87].”

    From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [87]

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Research works printed but not linked: 1

  1. [25]Sitheswaran R Malarkhodi S. (2014)., In: 2014 International Conference on Electronics and Communication Systems (ICECS).

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Authors
Barrera, F. J., Brown, E. D. L., Rojo, A., Obeso, J., Plata, H., Lincango, E. P., Terry, N., Rodríguez-Gutiérrez, R., Hall, J. E., & Shekhar, S.
Affiliations
  • Harvard University (US)
  • National Institutes of Health (US)
  • Universidad Autónoma de Nuevo León (MX)
Corresponding author
Shekhar, Skand
ORCID
Venue
Frontiers in Endocrinology
Publisher
Frontiers Media SA
Published
2023-09-18
Volume
14
Pages
1106625-1106625
ISSN
1664-2392 (ISSN-L)
DOI
10.3389/fendo.2023.1106625
PMC
PMC10542899
OpenAlex
W4386852362
Cited by
91
References
66
Citations by year
2026: 41 · 2025: 36 · 2024: 14
FWCI
3.69
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Endocrinology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institutes of Health
  • National Institute of Environmental Health Sciences (ZIAES103323, ZIDES102465)
Topics
  • Health Sciences › Medicine › Reproductive Medicine › Ovarian function and disorders
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
polycystic ovary syndrome, artificial intelligence, machine learning, PCOS diagnosis, PCOS classification, Rotterdam criteria, diagnostic accuracy, electronic health records, support vector machine, early diagnosis
MeSH terms
Machine Learning, Artificial Intelligence, Female, Humans, Polycystic Ovary Syndrome, Cluster Analysis, Proteomics

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