Medical Imaging

Applied a pretrained CNN (transferred from natural image recognition, e.g. ResNet/Inception-like architecture) to 4.6 million cardiac MRI images from UK Biobank, trained on only 116 manually annotated samples to regress ascending and descending thoracic aorta dimensions. GWAS identified 82 ascending and 47 descending aorta loci. Demonstrates transfer learning from natural images to medical imaging for rapid biobank-scale phenotyping.

deep learning, CNN, ImageNet transfer learning, cardiac MRI, thoracic aorta, image regression, UK Biobank

Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter. Transcriptome-wide analyses, rare-variant burden tests and human aortic single nucleus RNA sequencing prioritized genes including FBN1 and MFAP5.