Bridging Genomics to Cardiology Clinical Practice: Artificial Intelligence in Optimizing Polygenic Risk Scores: A Systematic Review.

ABSTRACT: Despite advances in cardiovascular disease risk stratification, traditional risk prediction models often fail to identify high-risk individuals before adverse events occur, underscoring the need for more precise tools. Polygenic risk scores (PRS) quantify genetic susceptibility by aggregating genetic variants but face challenges in practice. This systematic review investigates how artificial intelligence (AI) and machine learning algorithms can optimize PRS (AI-optimized PRS) to improve cardiovascular disease prediction. Analyzing 13 studies, we found that AI-optimized PRS models enhance predictive accuracy by improving feature selection, handling high-dimensional data, and integrating diverse variables-including clinical risk factors, biomarkers, imaging, and combining multiple PRS. These

SUBMITTER: Hosseini K 

PROVIDER: S-EPMC12277611 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature