Artificial Intelligence and Machine Learning Could Revolutionize PCOS Diagnosis, According to NIH Study
Poly Cystic Ovarian Syndrome (PCOS), the most prevalent hormone problem affecting women, could be efficiently and accurately diagnosed using Artificial Intelligence (AI) and Machine Learning (ML), claims a recent study conducted by the National Institutes of Health (NIH).
PCOS is a hormonal disorder that commonly affects women between the ages of 15 and 45. It is characterized by improper ovarian function and often accompanied by high testosterone levels. Symptoms of PCOS include irregular menstrual cycles, acne, excessive facial hair, and head hair loss. Additionally, women with PCOS are at an increased risk of developing Type 2 diabetes, sleep disorders, psychological issues, heart disease, uterine cancer, and infertility.
However, diagnosing PCOS can be challenging due to its overlap with other conditions. To address this issue, researchers examined the use of AI and ML in diagnosing and categorizing PCOS. The study found that AI and ML programs were highly effective in identifying patients at risk for PCOS.
“The effectiveness of AI and machine learning in detecting PCOS was even more impressive than we had thought,” said Dr. Janet Hall, senior investigator and endocrinologist at the National Institute of Environmental Health Sciences (NIEHS), part of NIH, and a study co-author.
The study authors suggest combining large population-based research with electronic health datasets to find diagnostic biomarkers that can aid in PCOS diagnosis. They also emphasize the importance of incorporating AI and ML in electronic health records and other clinical settings to improve the diagnosis and care of women with PCOS.
AI is particularly helpful in diagnosing conditions like PCOS that are challenging to identify due to its ability to handle vast amounts of diverse data, such as that collected from electronic health records.
During the study, researchers thoroughly examined peer-reviewed studies conducted from 1997 to 2022 that used AI and ML to identify PCOS. The researchers found that the detection accuracy of PCOS using AI and ML ranged from 80 to 90% across the studies that employed standardized diagnostic criteria.
“The high performance of AI/ML in detecting PCOS is the most important takeaway of our study,” said Dr. Skand Shekhar, senior author of the study and assistant research physician and endocrinologist at the NIEHS.
The integration of AI and ML in PCOS diagnosis could lead to early detection, resulting in financial savings and a lighter burden on patients and the healthcare system. However, further studies with strong validation and testing procedures are needed to ensure the seamless integration of AI and ML for chronic health disorders like PCOS.
Overall, the study highlights the potential of AI and ML to revolutionize the diagnosis and management of PCOS, improving the lives of countless women affected by this condition.