Imperial College London
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DrAntonioSimoes Monteiro de Marvao

Faculty of Medicine, Institute of Clinical Sciences

Honorary Clinical Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 3313 1510antonio.de-marvao

 
 
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Location

 

Robert Steiner MRI UnitHammersmith HospitalHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{de:2020:10.3389/fcvm.2019.00195,
author = {de, Marvao A and Dawes, TJW and O'Regan, DP},
doi = {10.3389/fcvm.2019.00195},
journal = {Frontiers in Cardiovascular Medicine},
pages = {1--10},
title = {Artificial intelligence for cardiac imaging-genetics research},
url = {http://dx.doi.org/10.3389/fcvm.2019.00195},
volume = {6},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - de,Marvao A
AU  - Dawes,TJW
AU  - O'Regan,DP
DO  - 10.3389/fcvm.2019.00195
EP  - 10
PY  - 2020///
SN  - 2297-055X
SP  - 1
TI  - Artificial intelligence for cardiac imaging-genetics research
T2  - Frontiers in Cardiovascular Medicine
UR  - http://dx.doi.org/10.3389/fcvm.2019.00195
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000510941900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.frontiersin.org/articles/10.3389/fcvm.2019.00195/full
UR  - http://hdl.handle.net/10044/1/76972
VL  - 6
ER  -
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