Artificial Intelligence and Cardiovascular Genetics

Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical he...

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Bibliographic Details
Published in:Life (Basel, Switzerland) Switzerland), 2022-02, Vol.12 (2), p.279
Main Authors: Krittanawong, Chayakrit, Johnson, Kipp W, Choi, Edward, Kaplin, Scott, Venner, Eric, Murugan, Mullai, Wang, Zhen, Glicksberg, Benjamin S, Amos, Christopher I, Schatz, Michael C, Tang, W H Wilson
Format: Article
Language:English
Subjects:
Algorithms
Amyloidosis
Angiography
Artificial intelligence
Biomarkers
cardiology
Cardiomyopathy
Cardiovascular disease
Cardiovascular diseases
Coronary vessels
Deep learning
Electronic health records
Family medical history
Genetic disorders
Genetic diversity
Genetics
Genomes
Genomic analysis
Genomics
Health risk assessment
Heterogeneity
Machine learning
Medical equipment
Medicine
New technology
Next-generation sequencing
Patients
Precision medicine
R&D
Research & development
Review
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Summary:Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.
ISSN:2075-1729
2075-1729
DOI:10.3390/life12020279