DIGITAL TWINS OF CARDIOVASCULAR BIOMECHANICS: HOW CAN WE ACHIEVE THIS FOR EACH PATIENT?

A. Aggarwal

University of Glasgow, United Kingdom

LEARNING DISEASE: FEASIBILITY OF MODELING OF MYOCARDIAL INFARCTION USING A NEURAL NETWORK FINITE ELEMENT APPROACH

S. Motiwale, M. S Sacks

University of Texas at Austin, United States of America

DEEP LEARNING-DRIVEN VOLUMETRIC VASCULATURE MODELING FOR 3D GROWTH MAPPING IN LONGITUDINAL MEDICAL IMAGING

C. Yan^1,2, B. C. Ayers³, S. Haltmeier^1,2, A. Rouhollahi¹, A. S. Jassar³, F. R. Nezami¹

¹Brigham and Women's Hospital, Harvard Medical School, United States of America; ²Institute for Biomedical Engineering, ETH Zurich, Switzerland; ³Massachusetts General Hospital, Harvard Medical School, United States of America

GRAPH NEURAL NETWORKS FOR REAL-TIME PREDICTION AND TREATMENT DECISION IN INTRACRANIAL ANEURYSMS

V. Lannelongue, U. Pelissier, P. Garnier, L. Philibert-Nicol, P. Jeken-Rico, P. Meliga, E. Hachem

Mines Paris PSL, France

GEOMETRIC DL-BASED PREDICTION OF AXIAL STRESSES TO ASSESS THE RISK OF ASCENDING AORTA DISSECTION

D. Astori¹, S. Di Filippo¹, S. Saitta^2,3, I. Ianniruberto¹, G. Nannini¹, D. Milesi¹, A. Redaelli¹, E. Votta¹

¹Politecnico di Milano, Italy; ²Amsterdam UMC, Netherlands; ³University of Amsterdam, Netherlands

DEEP LEARNING GENERATION OF INTRACRANIAL ANEURYSM AT CONTROLLED MORPHOLOGY FOR FLUID DYNAMICS INVESTIGATIONS

W. Ding¹, K. Ji¹, S. Castro², Y. Luo¹, C. H. Yap¹

¹Imperial College London, United Kingdom; ²Instituto Superior Técnico, Portugal

A RADIOMECHANICAL MACHINE LEARNING APPROACH FOR HIGH-RISK CAROTID PLAQUE PREDICTION

A. Corti¹, J. de Kort^1,2, D. Bissacco², M. Domanin², S. Trimarchi², V. Corino^1,3, F. Migliavacca¹, L. Mainardi¹

¹Politecnico di Milano, Italy; ²Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, Italy; ³Centro Cardiologico Monzino IRCCS, Italy