Cardiac MRI

Applied a CNN-based segmentation model (U-Net style architecture) to automatically segment left ventricular myocardium from cardiac MRI in 43,230 UK Biobank participants. The segmented contours were used to compute left ventricular mass indexed to body surface area (LVMI), enabling GWAS that identified 12 associations (11 novel) implicating genes associated with cardiac contractility and cardiomyopathy. The LVMI polygenic risk score validated in independent Mass General Brigham cohort.

deep learning, cardiac MRI segmentation, U-Net, left ventricular mass, CNN, cardiomyopathy, UK Biobank

Left ventricular mass is a risk marker for cardiovascular events, and may indicate an underlying cardiomyopathy. Cardiac magnetic resonance is the gold-standard for left ventricular mass estimation, but is challenging to obtain at scale. Here, we use deep learning to enable genome-wide association study of cardiac magnetic resonance-derived left ventricular mass indexed to body surface area within 43,230 UK Biobank participants. We identify 12 genome-wide associations (1 known at TTN and 11 novel for left ventricular mass).

Built a CNN-based deep learning algorithm for automated segmentation of left ventricular myocardium from cardiac MRI, enabling precise calculation of 12 regional wall thickness (LVRWT) measurements in 42,194 UK Biobank participants. GWAS of these 12 CNN-derived LVRWT traits identified 72 significant genetic loci involved in heart development and contraction pathways. Mendelian randomization confirmed causal relationships with hypertrophic cardiomyopathy. The PRS of inferoseptal LVRWT enabled identification of high-risk individuals.

deep learning, cardiac MRI, CNN, left ventricular wall thickness segmentation, hypertrophic cardiomyopathy, UK Biobank

Left ventricular regional wall thickness (LVRWT) is an independent predictor of morbidity and mortality in cardiovascular diseases (CVDs). To identify specific genetic influences on individual LVRWT, we established a novel deep learning algorithm to calculate 12 LVRWTs accurately in 42,194 individuals from the UK Biobank with cardiac magnetic resonance (CMR) imaging. Genome-wide association studies of CMR-derived 12 LVRWTs identified 72 significant genetic loci associated with at least one LVRWT phenotype.