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

• Published in: Frontiers in endocrinology (Lausanne) Vol. 14; p. 1106625
• Main Authors: Barrera, Francisco J, Brown, Ethan D L, Rojo, Amanda, Obeso, Javier, Plata, Hiram, Lincango, Eddy P, Terry, Nancy, Rodríguez-Gutiérrez, René, Hall, Janet E, Shekhar, Skand
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 18.09.2023
• Subjects: Artificial Intelligence; classification; Cluster Analysis; diagnosis; Endocrinology; Female; Humans; Machine Learning; polycystic ovarian syndrome (PCOS); Polycystic Ovary Syndrome - diagnosis; Proteomics; Stein-Leventhal syndrome; diagnosis; polycystic ovarian syndrome (PCOS); classification; machine learning; Stein-Leventhal syndrome; artificial intelligence
• ISSN: 1664-2392; 1664-2392
• DOI: 10.3389/fendo.2023.1106625

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. 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. 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). 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. https://www.crd.york.ac.uk/prospero/, identifier CRD42022295287.