TL;DR:
- Artificial intelligence and machine learning demonstrate high performance in detecting polycystic ovary syndrome (PCOS).
- Standardized diagnostic criteria enhance the usefulness of AI and machine learning in PCOS detection.
- A systematic review and meta-analysis identified 31 studies evaluating AI/machine learning in PCOS detection.
- Clinical data with or without imaging were used in 55% of the studies.
- Support vector machine, K-nearest neighbor, and regression models were the most commonly employed AI/machine learning techniques.
- AI and machine learning showed excellent performance in distinguishing PCOS cases from non-PCOS cases.
- Future studies should incorporate AI into electronic health record settings and involve clinician input for effective clinical care.
- AI and machine learning offers a promising avenue for improving the diagnosis and well-being of women with PCOS.
Main AI News:
Artificial intelligence (AI) and machine learning have demonstrated remarkable capabilities in diagnosing and categorizing polycystic ovary syndrome (PCOS), as indicated by research findings. To optimize the utility of AI and machine learning as detection tools for PCOS and minimize diagnostic delays, the implementation of standardized diagnostic criteria is crucial. This was emphasized by a speaker at the annual conference of the American Association of Clinical Endocrinology.
Dr. Skand Shekhar, an assistant research physician and endocrinologist at the National Institute of Environmental Health Sciences at NIH, highlighted the key takeaway from their study. The remarkable performance of AI and machine learning in detecting PCOS was evident across various diagnostic and classification methods.
Particularly noteworthy were the results obtained when employing standardized criteria for assessing the performance of AI models in PCOS diagnoses, such as the international PCOS criteria and Rotterdam criteria. These criteria enabled AI/machine learning to effectively differentiate between individuals with PCOS and those without.
In their systematic review and meta-analysis, Dr. Shekhar and his colleagues meticulously combed through multiple databases, including Embase, Cochrane Register, Web of Science, and the Institute of Electrical and Electronics Engineers (IEEE) Xplore Digital Library. They identified 31 observational studies conducted between the study’s inception and January 2022 that examined the performance of AI/machine learning in detecting PCOS. To diagnose PCOS, studies utilizing recognized clinical PCOS diagnostic criteria (such as NIH, Rotterdam, or Revised International PCOS classification) were considered, while studies lacking these criteria were categorized as classifying PCOS.
The selected studies encompassed diverse participant numbers, ranging from 9 to 2,000. Among the included studies, 23% were multicenter studies, predominantly conducted in India (29%) or China (16%). The median age of PCOS participants was 29 years. Approximately 32% of the studies diagnosed PCOS based on established diagnostic criteria, while the remaining 68% classified PCOS.
In 55% of the studies, clinical data with or without imaging were employed. The most prevalent AI/machine learning techniques utilized were support vector machine (42%), K-nearest neighbor (26%), and regression models (23%). The researchers observed varying degrees of efficacy across the studies, with the area under the receiver operating characteristic curve ranging between 73% and 100% in seven studies, diagnostic accuracy of 89% to 100% in four studies, sensitivity ranging from 41% to 100% in 10 studies, specificity between 75% to 100% in 10 studies, positive predictive value between 68% and 95% in four studies, and negative predictive value between 94% and 99% in two studies.
Dr. Shekhar expressed his pleasant surprise at the remarkably high effectiveness of AI and machine learning in detecting PCOS, encompassing various technologies and data sources. These findings hold immense promise for advancing the health and well-being of millions of women worldwide who either remain undiagnosed or experience delays in obtaining a PCOS diagnosis.
To fully harness the potential of these technologies in the clinical care of individuals with PCOS, future studies should explore the integration of AI into electronic health record settings and involve increased clinician input. This will ensure that these technologies are effectively employed to enhance the clinical management of PCOS patients.
Reiterating the significance of their findings, Dr. Shekhar emphasized the emergence of an exciting new avenue to address the healthcare needs of women affected by PCOS. With the aid of AI and machine learning, the diagnosis and management of PCOS can be significantly improved, potentially transforming the lives of countless women worldwide.
Conlcusion:
The remarkable performance of artificial intelligence (AI) and machine learning in diagnosing and classifying polycystic ovary syndrome (PCOS) holds significant implications for the market. The utilization of standardized diagnostic criteria enhances the utility of AI and machine learning as effective tools for detecting PCOS and reducing diagnostic delays.
This breakthrough technology opens up new opportunities for healthcare providers and companies operating in the PCOS market to develop innovative solutions and services. By leveraging AI and machine learning, these market players can enhance the accuracy and efficiency of PCOS diagnosis, leading to improved patient outcomes and an overall positive impact on the market’s growth and competitiveness.
Moreover, the potential for integrating AI into electronic health record systems presents an avenue for streamlining clinical workflows and facilitating personalized care for PCOS patients. As the market continues to embrace AI and machine learning, it is poised to witness transformative advancements that address the unmet needs of millions of women worldwide suffering from PCOS.
