ID: 912
BONE REMODELLING WITH ARTIFICIAL NEURAL NETWORKS
A. Pais1, J. Lino Alves1,2, J. Belinha3
1INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering, Portugal; 2FEUP - Faculty of Engineering, University of Porto; 3ISEP - School of Engineering, Polytechnic of Porto
ID: 569
OPTIMIZATION AND INDUSTRIALIZATION OF A METABOLIC HOLTER DEVICE AND SOFTWARE DEVELOPMENT
E. Bori1, M. Mouton1, C. De Asmundis2, R. Cannataro3, B. Innocenti1
1BEAMS Department (Bio Electro and Mechanical Systems), École Polytechnique de Bruxelles, Université Libre de Bruxelles, Brussel, Belgium; 2Heart Rhythm Management Centre, Universitair Ziekenhuis Brussel–Vrije Universiteit Brussel, Brussel, Belgium; 3Galascreen Laboratories, University of Calabria, Rende, Italy
ID: 178
DEEP LEARNING APPROACH FOR IN-STENT RESTENOSIS USING BIOLOGICALLY-INFORMED NEURAL NETWORKS
J. Shi, K. Manjunatha, S. Reese
Institute of Applied Mechanics, RWTH Aachen University, Germany
ID: 355
PRE-TRAINING VARIED VASCULAR GEOMETRIES WITH A DEEP LEARNING SIDE NETWORK IN PHYSICS-INFORMED NEURAL NETWORK SIMULATIONS OF VASCULAR FLUID DYNAMICS
H. S. Wong, B. Li, W. X. Chan, C. H. Yap
Department of Bioengineering, Imperial College London, United Kingdom
ID: 891
2D-UNET BASED APPROACH FOR 3D SEGMENTATION OF CORONARY ARTERY FROM COMPUTED TOMOGRAPHY ANGIOGRAPHY
G. Nannini1, S. Saitta1, A. Baggiano2, G. Pontone2, A. Redaelli1
1Politecnico di Milano, Italy; 2Centro Cardiologico Monzino, Italy
ID: 356
CFD-BASED SYNTHETIC DATA GENERATION FOR MACHINE LEARNING BASED PRESSURE DROP ASSESSMENT IN AORTIC STENOSIS
T. I. Matei1,2, A. B. Popescu1,2, C. I. Nita1, C. F. Ciusdel1,2, L. M. Itu1,2
1Transilvania University of Brasov, Romania; 2Siemens SRL, Romania
ID: 281
SINE-BASED ACTIVATION FUNCTION IS SUPERIOR IN PHYSICS-INFORMED NEURAL NETWORK FOR CARDIOVASCULAR FLOWS
A. Aghaee, M. O. Khan
Department Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
ID: 376
THREE-DIMENSIONAL FLOW RECONSTRUCTION IN A DISSECTED AORTA FROM 4D-MRI DATA
D. Ahmed1, C. Stokes2,3, N. Lind4, F. Haupt4, D. Becker5, V. Muthurangu6, H. Von Tengg-Kobligk4, S. Balabani2,3, V. Diaz-Zuccarini2,3, G. Papadakis1
1Department of Aeronautics, Imperial College London, United Kingdom; 2Department of Mechanical Engineering, University College London, United Kingdom; 3Wellcome-EPSRC Centre for Interventional Surgical Sciences, London, United Kingdom; 4Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern, Switzerland.; 5Clinic of Vascular Surgery, Inselspital, University of Bern, Switzerland; 6Centre for Translational Cardiovascular Imaging, University College London, United Kingdom
ID: 699
THE HARD REALITY OF SCATTERED DATA TO PREDICT HEART RHYTHM DISORDERS
C. M. Buck1, M. A. de Winter1, A. G. de Lepper2, M. van 't Veer1,2, W. Huberts2,3, F. N. van de Vosse1, L. R. Dekker1,2
1Department of Biomedical Engineering, Eindhoven University of Technology, the Netherlands; 2Department of Cardiology, Catharina Hospital Eindhoven, the Netherlands; 3Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, the Netherlands
ID: 911
REVERSE HOMOGENIZATION USING NEURAL NETWORKS FOR STRESS SHIELDING MINIMIZATION
A. Pais1, J. Lino Alves1,2, J. Belinha3
1INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering, Portugal; 2FEUP - Faculty of Engineering, University of Porto; 3ISEP - School of Engineering, Polytechnic of Porto
ID: 654
PARAMETER FITTING FOR A VISCOELASTIC CONSTITUTIVE MODEL USING A MACHINE LEARNING MODEL
M. Barra1, C. Garcia-Herrera1, D. Celentano2, F. Sahli2, E. Herrera3
1Universidad de Santiago de Chile, Chile; 2Pontificia Universidad Católica de Chile, Chile; 3Universidad de Chile, Chile
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