Conference Agenda

BONE REMODELLING WITH ARTIFICIAL NEURAL NETWORKS

A. Pais¹, J. Lino Alves^1,2, 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

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

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

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

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

CFD-BASED SYNTHETIC DATA GENERATION FOR MACHINE LEARNING BASED PRESSURE DROP ASSESSMENT IN AORTIC STENOSIS

T. I. Matei^1,2, A. B. Popescu^1,2, C. I. Nita¹, C. F. Ciusdel^1,2, L. M. Itu^1,2

¹Transilvania University of Brasov, Romania; ²Siemens SRL, Romania

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

THREE-DIMENSIONAL FLOW RECONSTRUCTION IN A DISSECTED AORTA FROM 4D-MRI DATA

D. Ahmed¹, C. Stokes^2,3, N. Lind⁴, F. Haupt⁴, D. Becker⁵, V. Muthurangu⁶, H. Von Tengg-Kobligk⁴, S. Balabani^2,3, V. Diaz-Zuccarini^2,3, 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

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 Veer^1,2, W. Huberts^2,3, F. N. van de Vosse¹, L. R. Dekker^1,2

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

REVERSE HOMOGENIZATION USING NEURAL NETWORKS FOR STRESS SHIELDING MINIMIZATION

A. Pais¹, J. Lino Alves^1,2, 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

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