TY  - JOUR
TI  - A systematic review on cardiovascular risk stratification in the artificial intelligence paradigm using radiogenomics
AU  - Singh, Manasvi
AU  - Khanna, Narendra
AU  - Nicolaides, Andrew
AU  - Sharma, Aditya
AU  - Gupta, Siddharth
AU  - Kitas, George
AU  - Singh, Inder M.
AU  - Isenović, Esma
AU  - Saba, Luca
AU  - Suri, Jasjit S.
PY  - 2026
JO  - Exploration of Cardiology
VL  - 4
SP  - 1012122
DO  - 10.37349/ec.2026.1012122
UR  - https://www.explorationpub.com/Journals/ec/Article/1012122
AB  - Background: The cardiovascular risk and patient management are evaluated on the basis of risk scores that account for the multifactorial risk factors and thus fail in the estimation of an individual’s cardiovascular disease (CVD) risk. Advances in the field of medical imaging, especially cardiac computed tomography angiography (CTA) and magnetic resonance imaging (MRI), have laid the foundation of radiogenomics. Methods: Research from the past 10 years was collected using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology from PubMed, Google Scholar, ScienceDirect, and MEDLINE, and some additional sources like websites and arXiv. Results: A total of 860 records were identified, and after the removal of duplicates, 800 remained for the screening for relevance by the abstracts and titles. After screening, 225 articles were assessed for eligibility, and the records that were either out of scope, had insufficient data, or were neither written nor translated in English were excluded. Finally, 80 records were identified to be included in the current review. Discussion: We speculated on the application of artificial intelligence (AI) algorithms to identify CVD and its prognosis. Radiomic features, which are extracted by CTA and MRI, have shown great diagnostic accuracy of coronary plaques, and on the other hand, some studies exploited radiogenomics integration, which suggests that further research needs to be done in this field.
ER  -