Bourantas, Christos;
Torii, Ryo;
Karabasov, Sergey;
Krams, Rob;
(2024)
Editorial: Computational modelling of cardiovascular hemodynamics and machine learning.
Frontiers in Cardiovascular Medicine
, 11
, Article 1355843. 10.3389/fcvm.2024.1355843.
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Abstract
Artificial Intelligence (A.I.) holds promises in many fields, especially in the health sector. Here, pattern recognition of complex problems—a major strength of A.I. is what makes A.I. so useful. Despite its promises, applying A.I. to the health sector comes with specific challenges, of which the papers in the current issue aim to provide solutions (1–3). This editorial will offer specific background theory on A.I. to better understand the solutions offered in this issue.
| Type: | Article |
|---|---|
| Title: | Editorial: Computational modelling of cardiovascular hemodynamics and machine learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fcvm.2024.1355843 |
| Publisher version: | http://dx.doi.org/10.3389/fcvm.2024.1355843 |
| Language: | English |
| Additional information: | Copyright © 2024 Bourantas, Torii, Karabasov and Krams. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://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. |
| Keywords: | Artificial intelligence, machine learning, shear stress, imaging, navier stokes solvers |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10189855 |
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