Comparing various methods of artificial intelligence in diagnosis of polycystic ovarian syndrome

Background and aims: polycystic ovarian syndrome (PCOS) is a common endocrine disorderin reproductive-aged women with risk of complex long-term complications. However, early detectionof PCOS is one of the most critical concern in the field of women’s healthcare. Owing tothe complexity and various heterogenic profiles of the syndrome, identification and differentialdiagnosis remain challenging despite the widely accepted criteria and tests. With the rapid developmentof artificial intelligence (AI), using machine learning and deep learning to assist withPCOS detection has attracted much more attention. In current study different AI methods appliedin PCOS diagnosis are reviewed to highlight the best prediction model(s).Method: The systematic review was performed on all published studies that have investigated theAI technology on PCOS detection based on the PRISMA statement, PubMed and Ovid databaseswere searched up to November ۲۰۲۲ using the terms ‘polycystic ovarian disease/syndrome’,‘PCOS’, ‘Stein Leventhal syndrome’, ‘Rotterdam’, ‘ESHRE/ASRM’, ‘criteria’, and ‘AI’, ‘deeplearning’, and ‘machine learning’ algorithms.Results: Based on the research design, algorithm type(s), number and types of clinical parameters,the detection accuracy of each method varied greatly, ranging ۷۹-۹۸%. Comparing thevarious applied AI methods showed that hybrid approaches are very much effective in detectionof PCOS, especially SVM/KNN/ Logistic Regression hybrid model with ۹۸% accuracy score.Moreover, XG Boost and CatBoost have been also proposed to function as strong models to detectionof PCOS with accuracy score of ۹۶% and ۹۵%, respectively.Conclusion: AI deep learning technology provides a powerful tool for detection PCOS at an earlystage and then early treatment of the patient’s. Ultimately, in order to achieve highest detectionaccuracy of PCOS, hybrid approach- based algorithms on the most important genetical, epigenetical,transcriptome, clinical, metabolic, and immunological characteristics should be assessed.

کلیدواژه ها:

PCOS ، artificial intelligence ، algorithms ، accuracy rate ، early detection