Artificial intelligence for cardiac imaging-genetics research
File(s)
Published version
Author(s)
de Marvao, Antonio
Dawes, Timothy JW
O'Regan, Declan P
Type
Journal Article
Abstract
Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
Date Issued
2020-01-21
Date Acceptance
2019-12-27
Citation
Frontiers in Cardiovascular Medicine, 2020, 6, pp.1-10
ISSN
2297-055X
Publisher
Frontiers Media
Start Page
1
End Page
10
Journal / Book Title
Frontiers in Cardiovascular Medicine
Volume
6
Copyright Statement
© 2020 de Marvao, Dawes and O'Regan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (http://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Sponsor
Imperial College Healthcare NHS Trust- BRC Funding
The Academy of Medical Sciences
Imperial College Healthcare NHS Trust- BRC Funding
British Heart Foundation
Imperial College Healthcare NHS Trust- BRC Funding
British Heart Foundation
British Heart Foundation
Imperial College London
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000510941900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
RD410
nil
RDC04
NH/17/1/32725
RDB02
RE/18/4/34215
RG/19/6/34387
Subjects
Science & Technology
Life Sciences & Biomedicine
Cardiac & Cardiovascular Systems
Cardiovascular System & Cardiology
artificial intelligence
machine learning
deep learning
genetics
genomics
imaging-genetics
cardiovascular imaging
cardiology
MACHINE-LEARNING ALGORITHMS
GENOME-WIDE ASSOCIATION
HEART-FAILURE
PULMONARY-HYPERTENSION
EJECTION FRACTION
BIG DATA
PREDICTION
CLASSIFICATION
IDENTIFICATION
FEASIBILITY
Publication Status
Published
Article Number
ARTN 195
Date Publish Online
2020-01-21