AI and machine learning dyad can successfully diagnose polycystic ovary syndrome

Artificial intelligence (AI) and machine learning (ML) can effectively detect and diagnose Polycystic Ovary Syndrome (PCOS), which is the most common hormone disorder among women, typically between ages 15 and 45, according to a new NIH study.

Study authors suggested integrating large population-based studies with electronic health datasets and analyzing common laboratory tests to identify sensitive diagnostic biomarkers that can facilitate the diagnosis of PCOS. Because some of the features of PCOS can co-occur with other disorders such as obesity, diabetes, and cardiometabolic disorders, it frequently goes unrecognized during diagnosis.

AI refers to the use of computer-based systems or tools to mimic human intelligence and to help make decisions or predictions. ML is a subdivision of AI focused on learning from previous events and applying this knowledge to future decision-making.

The researchers conducted a systematic review of all peer-reviewed studies published on this topic for the past 25 years (1997-2022) that used AI/ML to detect PCOS. With the help of an experienced NIH librarian, the researchers identified potentially eligible studies. In total, they screened 135 studies and included 31 in this paper. All studies were observational and assessed the use of AI/ML technologies on patient diagnosis. Ultrasound images were included in about half the studies. The average age of the participants in the studies was 29.

Among the 10 studies that used standardized diagnostic criteria to diagnose PCOS, the accuracy of detection ranged from 80-90%.

Reference: Barrera FJ, Brown EDL, Rojo A, Obeso J, Plata H, Lincango EP, Terry N, Rodríguez-Gutiérrez R, Hall JE, Shekhar S, 2023. Application of Machine Learning and Artificial Intelligence in the Diagnosis and Classification of Polycystic Ovarian Syndrome: A Systematic Review. Frontiers in Endocrinology. https://www.frontiersin.org/articles/10.3389/fendo.2023.1106625/full