AI and machine learning 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 study by the National Institutes of Health. Researchers systematically reviewed published scientific studies that used AI/ML to analyze data to diagnose and classify PCOS and found that AI/ML based programs were able to successfully detect PCOS.
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.
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. AI can process massive amounts of distinct data, such as that derived from electronic health records, making it an ideal aid in the diagnosis of difficult to diagnose disorders like PCOS.
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%.
