Antonio de Marvao|Timothy Dawes|Declan P. O’Regan
Artificial Intelligence for Cardiac Imaging-Genetics Research
MRC London Institute of Medical Sciences|Imperial College London|MRC London Institute of Medical Sciences|Imperial College London|MRC London Institute of Medical Sciences|Imperial College London
cardiovascular conditions remain leading cause mortality morbidity worldwide genotype significant influence disease risk cardiac imaginggenetics aims identify characterise genetic variants influence functional physiological anatomical phenotypes derived cardiovascular imaging highthroughput dna sequencing genotyping greatly accelerated genetic discovery making variant interpretation one key challenges contemporary clinical genetics heterogeneous lowfidelity phenotyping difficulties integrating analysing largescale genetic imaging clinical datasets using traditional statistical approaches impeded process artificial intelligence ai methods deep learning particularly suited tackle challenges scalability high dimensionality data show promise field cardiac imaginggenetics review current state ai applied imaginggenetics research discuss outstanding methodological challenges field moves pilot studies mainstream applications one dimensional global descriptors highresolution models wholeorgan shape function univariate multivariate analysis candidate gene genomewide approaches finally consider future directions prospects ai imaginggenetics ultimately helping understand genetic environmental underpinnings cardiovascular health disease