Letture consigliate
N. Ahmadi et al. Physics-Informed Machine Learning in Biomedical Science and Engineering. Annu. Rev. Biomed. Eng. 2026; 28:309-336
A. Arzani et al. Machine learning for cardiovascular biomechanics modeling: challenges and beyond. Annals Biomed. Eng. 2022; 50(6), 615–627
C. M. Augustin et al. A computationally efficient physiologically comprehensive 3D–0D closed-loop model of the heart and circulation Comput. Meth. Appl. Mech. Eng. 2021; 386: 114092
P. Boyle et al. Fibrosis, atrial fibrillation and stroke: clinical updates and emerging mechanistic models. Heart 2021;107:99-105
A. L. Brown et al. Cardiac mechanics modeling: recent developments and current challenges. J. Elasticity 2026; 158, 28
C. Chen et al. Deep learning for cardiac image segmentation: A review, Frontiers Cardiovasc. Med., 7, 2020
A. De Vechi et al. Digital Twins for Predictive Modelling of Thrombosis and Stroke Risk. Thromb. Haemost. 2026; 126(08): 775-794
K. Gillette et al. A framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs. Med. Image Anal. 2021; 71: 102080
G. Plank et al. The openCARP simulation environment for cardiac electrophysiology. Comput. Meth. Prog. Biomed. 2021;208:106223
D. Qiu et al., “Local Mechanical Characterization of Cardiovascular Tissues: Methods, Challenges, and Pathways to Clinical Use.” Frontiers Mech. Eng. 2025; 11:1703081
R. Verzicco. Electro-fluid-mechanics of the heart. J. Fluid Mech., 2022; 941, P1