Advancements in Artificial Intelligence and Data Science for Cardiovascular Health

Dear Colleagues,

Cardiovascular disease is the highest cause of mortality worldwide. Recent advances in molecular epidemiological methods and techniques have remarkably enhanced cardiovascular health. Techniques such as genomics, metabolomics, proteomics, and transcriptomics have generated a large amount of data.

In addition, novel analytical approaches in genomics, such as genome-wide association analysis, genetic correlation, and gene-set enrichment analysis, have emerged in the last decade, improving our understanding of the biological pathways involved in cardiovascular diseases. Methods such as Mendelian randomization analysis and polygenic risk scores have improved our insight into causal factors. Machine learning approaches are increasingly being applied in the field and provide a better understanding of various predictors of cardiovascular diseases. In this issue, we are seeking to obtain insight into the advances that these methods and techniques have brought into the field.

The topics of interest include, but are not limited to, the following:

• Accelerating patient benefit in cardiology using artificial intelligence;
• Artificial intelligence for public health;
• Cardiac image processing;
• Data mining in cardiology;
• Decision support systems in cardiovascular health;
• Digital cardiology;
• Machine learning and deep learning methods in cardiovascular health;
• Machine learning in drug development in cardiology;
• Machine learning to handle cardiology hospital records;
• Personalised cardiology using machine learning;
• The prediction of cardiovascular risk (hypertension, myocardial infarction, atherosclerosis, stroke, etc.).

Dr. Raha Pazoki
Guest Editor

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