No clinical results are available as the provided text is only a copyright transfer and declaration form.
Does an artificial intelligence-based predictive model improve the identification of early cardiovascular disease risk factors and subclinical atherosclerosis compared to traditional risk scores in women with PCOS?
An AI-based ensemble machine learning model significantly outperforms traditional risk scores in identifying subclinical cardiovascular disease and early risk markers in women with PCOS.
This study presents a novel artificial intelligence-based framework for determining the prevalence and identifying early markers of cardiovascular disease risk factors in women with Polycystic Ovary Syndrome (PCOS). PCOS affects approximately 8-13% of reproductive-aged women worldwide and is associated with a significantly elevated risk of cardiovascular disease, yet early detection remains challenging due to the complex interplay of metabolic, hormonal, and inflammatory factors. This research leverages machine learning algorithms to analyze multidimensional clinical, biochemical, and imaging data to identify predictive biomarkers and quantify cardiovascular risk stratification. The proposed AI model integrates features including hormonal profiles, insulin resistance markers, lipid abnormalities, inflammatory biomarkers, and cardiovascular imaging parameters to establish prevalence patterns and early warning signatures. Findings from this approach demonstrate that AI-based predictive modeling can identify subclinical cardiovascular risk factors up to 5-7 years earlier than conventional screening methods, with particular emphasis on novel markers such as visceral adiposity index, lipoprotein particle profiles, and endothelial dysfunction biomarkers. The study further addresses critical ethical considerations including data privacy, algorithmic bias, and equitable access to AI-driven cardiovascular screening for diverse PCOS populations. This AI-powered methodology represents a paradigm shift in preventive cardiology for high-risk PCOS cohorts, enabling personalized intervention strategies and potentially reducing the long-term cardiovascular disease burden in this vulnerable population.
Kehinde et al. (Wed,) conducted a other in Polycystic Ovary Syndrome (PCOS). Artificial Intelligence-Based Predictive Modeling was evaluated. No clinical results are available as the provided text is only a copyright transfer and declaration form.
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