Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review

• Main Authors: Leila Shahmoradi (Author), Leila Azadbakht (Author), Jebraeil Farzi (Author), Sharareh Rostam Niakan Kalhori (Author), Alireza Banaye Yazdipour (Author), Fahimeh Solat (Author)
• Format: Book
• Published: BMC, 2024-04-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12905-024-03074-3; 1472-6874
• Keywords: Polycystic ovary syndrome; Artificial intelligence; Application; Decision support system; Nutrition recommender system; Gynecology and obstetrics; RG1-991; Public aspects of medicine; RA1-1270
• Source: BMC Women's Health, Vol 24, Iss 1, Pp 1-26 (2024)

Abstract Background People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, especially following the principles of healthy eating. The purpose of this study is to review artificial intelligence-based systems for providing management recommendations, especially food recommendations. Materials and methods This study started by searching three databases: PubMed, Scopus, and Web of Science, from inception until 6 June 2023. The result was the retrieval of 15,064 articles. First, we removed duplicate studies. After the title and abstract screening, 119 articles remained. Finally, after reviewing the full text of the articles and considering the inclusion and exclusion criteria, 20 studies were selected for the study. To assess the quality of articles, we used criteria proposed by Malhotra, Wen, and Kitchenham. Out of the total number of included studies, seventeen studies were high quality, while three studies were moderate quality. Results Most studies were conducted in India in 2021. Out of all the studies, diagnostic recommendation systems were the most frequently researched, accounting for 86% of the total. Precision, sensitivity, specificity, and accuracy were more common than other performance metrics. The most significant challenge or limitation encountered in these studies was the small sample size. Conclusion Recommender systems based on artificial intelligence can help in fields such as prediction, diagnosis, and management of polycystic ovary syndrome. Therefore, since there are no nutritional recommendation systems for these patients in Iran, this study can serve as a starting point for such research.