From:  A systematic review on cardiovascular risk stratification in the artificial intelligence paradigm using radiogenomics

 Comprehensive assessment of studies included in the review.

Study populationClinical domainData sourceAI model typeOutcomeNo. of studiesReferences
Adults with CVDsCVDImagingRadiomics-based MLRisk prediction/plaque characterization4[22, 26, 31, 41]
Adults with CVDsCVDImagingDLDiagnosis/screening5[15, 5659]
Adults with CVDsCVDImagingTraditional MLRisk prediction2[6, 60]
Adults with CVDsCVDGenomicsPRS modelsCVD risk prediction2[4, 54]
Adults with CVDsCVDGenomicsMLGenotype-phenotype prediction1[23]
Adults with CVDsCVDECG/wearablesDLScreening/detection3[57, 58, 61]
Adults with CVDsCVDEHR/clinical recordsDLPrognosis/outcome prediction1[62]
Mixed population cohortsCVDMulti-omicsDLPrecision medicine2[49, 50]
General CVD populationsCVDConceptual Conceptual/AI frameworksPrecision cardiology5[7, 10, 11, 34, 35]
Cancer patientsOncologyImagingCNNDiagnosis/molecular subtype prediction4[14, 6365]
Cancer patientsOncologyImagingRadiomics-based MLPrognosis/treatment response 5[1820, 63, 66]

CVD: cardiovascular disease; CNN: convolutional neural network; DL: deep learning; ECG: electrocardiogram; EHR: electronic health records; ML: machine learning; PRS: polygenic risk score.