Principal Investigator: Dr James Priest
Department: Division of Pediatric Cardiology
Stanford University, Division of Pediatric Cardiology, 750 Welch Road, Palo Alto CA 94304, United StatesTags: 15860, cardiovascular, genetics
Lead Collaborators: 1) Professor Erik Ingelsson
Collaborating Institutions and Addresses: 1) Uppsala University, Department of Medical Sciences, UCR/MTC, Dag Hammarskjölds väg 14B, Uppsala 75237, Sweden
Funding body: Internally funded out of PI’s existing research funds
1a: We aim to better understand the contribution of genetic factors to variation in normal heart structure and to look for genes and genetic variation which may contribute to diseases involving the structures and valves of the heart.
1b: These studies aim to better understand the genetics of normal cardiac structures and their malformations. Oftentimes these diseases occur in infancy or early childhood, and though the UK Biobank does not include children, studying these diseases in the enrolled subjects will improve our understanding of these diseases thus improving the prevention, diagnosis and treatment of cardiac structural malformations.
1c: We will look at the genetic information collected by the UK Biobank and correlate that information with normal differences in size of cardiac structures (measured by MRI) or with a particular disease involving structures of the heart.
1d: We will require access to the full cohort, but for selected aspects of the studies may only use a small subset affected with a particular disease or selected as a matched control subject.
PROJECT EXTENSION APPROVED 13.11.2017:
“Measurements of cardiac structures are being performed with the application of cutting edge techniques of machine learning and computer vision. The performance of measurement and classification data derived from such techniques are improved when multiple data sources are included, which includes the visualization of a particular cardiac structure from different angles, the use of standard transformations of visual data, and functional measurements such as shMOLLI. For this reason we request the use of fields pertaining to cardiac data (20207, 20210, 20211, 20213, 20214) to obtain additional data substrates for our efforts at automated measurement using computer vision techniques. The use all available cardiac MRI data will ensure the most accurate measurements are performed and the greatest utility is derived from the cardiac imaging data.”