Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization
Shuang Qian(King's College London), Steven Niederer(Lung Institute), Reza Razavi(King's College London), Hassan Zaidi(King's College London), Ludovica Cicci(Imperial College London), Daniel Hammersley(King's College London), Gernot Plank(Medical University of Graz), Yu Deng(Hong Kong University of Science and Technology), Elliot Fairweather(King's College London), Brian P. Halliday(Harefield Hospital), Martin J. Bishop(King's College London), Alistair A. Young(St Thomas' Hospital), Edward J. Vigmond(Université de Bordeaux), Laura Dal Toso(ETH Zurich), X Liu(Science and Technology Facilities Council), Richard E. Jones(Harefield Hospital), Pablo Lamata(King's College London), Marina Strocchi(King's College London), Sanjay Prasad(Harefield Hospital), Devran Uğurlu(Turing Institute)
Cited by 31
Related Papers
The ‘Digital Twin’ to enable the vision of precision cardiology
|European Heart Journal|2020|811
A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
|Medical Image Analysis|2020|475
Computational models in cardiology
|Nature Reviews Cardiology|2018|417
Verification of cardiac tissue electrophysiology simulators using an <i>N</i> -version benchmark
|Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences|2011|273
Scaling digital twins from the artisanal to the industrial
|Nature Computational Science|2021|218