Conference Agenda

INTEGRATING PERSONALISED MODELS, WEARABLES, AND DEEP LEARNING FOR PREDICTING FOOT BONE STRESS DURING RUNNING

L. Xiang¹, Z. Gao², V. Shim¹, A. Wang¹, Y. Gu^3,4, J. Fernandez^1,4

¹Auckland Bioengineering Institute, University of Auckland, New Zealand; ²Faculty of Engineering, University of Pannonia, Hungary; ³Faculty of Sports Science, Ningbo University, China; ⁴Department of Engineering Science & Biomedical Engineering, University of Auckland, New Zealand

IDENTIFICATION OF HYPERELASTICITY IN HUMAN ARTERIES USING A MACHINE LEARNING BASED VIRTUAL FIELDS METHOD

s. meng, A. A. Karkhaneh Yousefi, S. Avril

Mines Saint etienne, France

DATA-DRIVEN STOCHASTIC MODEL FOR GENERATING REALISTIC BONE MORPHOLOGY.

P. Henyš¹, M. Vořechovský¹, M. Kuchař¹, N. Hammer², B. Ondruschka³

¹Technical University of Liberec, Czech Republic; ²Medizinische Universität Graz, Austria; ³Universitätsklinikum Hamburg-Eppendorf, Germany

WALL SHEAR STRESS PREDICTION ON A PATIENT-SPECIFIC FEMORAL ARTERY USING A ROM-BASED ML MODEL

C. Chatpattanasiri^1,2, F. Ninno^2,3, V. Diaz-Zuccarini^1,2, S. Balabani^1,2

¹Department of Mechanical Engineering, University College London, UK; ²Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; ³Department of Medical Physics and Biomedical Engineering, University College London, UK

A MACHINE LEARNING APPROACH TO PREDICT IN VIVO SKIN GROWTH

M. Nagle^1,2, H. Conroy Broderick², A. Buganza Tepole³, M. Fop⁴, A. Ní Annaidh^2,5

¹SFI CRT in Foundations of Data Science, Ireland; ²School of Mechanical and Materials Engineering, UCD, Ireland; ³School of Mechanical Engineering, Purdue University, USA; ⁴School of Mathematics and Statistics, UCD, Ireland; ⁵Charles Institute of Dermatology, UCD, Ireland

DATA-DRIVEN CONSTITUTIVE MODELING BASED ON GAUSSIAN PROCESSES AND NONLINEAR DIMENSIONALITY REDUCTION

A. Aggarwal¹, B. S. Jensen², C.-H. Lee³

¹University of Glasgow, United Kingdom; ²Technical University of Denmark, Denmark; ³University of California, Riverside, USA

CGAN-BASED SYNTHETIC AUGMENTATION FOR SMALL 4D FLOW PC MRA DATASET SEGMENTATION

S. Garzia^1,2, M. A. Scarpolini^1,3, M. Mazzoli^1,2, K. Capellini¹, A. Monteleone⁴, F. Cademartiri⁴, S. Celi¹

¹BioCardioLab, Bioengineering Unit, Fondazione Monasterio, Italy; ²Department of Information Engineering, University of Pisa, Italy; ³Department of Industrial Engineering, University of Rome "Tor Vergata", Italy; ⁴Clinical Imaging Department, Fondazione Monasterio, Italy

GAIT CONTEXTUALIZATION USING MULTI-LAYER PERCEPTRONS, MORE FEATURES DOES NOT GUARANTEE INCREASED ACCURACY

A. Dzewaltowski¹, S. Hwang², I. Ustun², U. Huzaifa², C. Connaboy¹

¹Rosalind Franklin University of Medicine & Science, United States of America; ²DePaul University, United States of America

ANALYSIS OF DEEP LEARNING CLASSIFICATION METHODS OF 3D KINEMATIC DATA FOR NEUROLOGICAL GAIT DISORDERS

Z. Lan^1,4, M. Lempereur^1,2,3, A. Aïssa-El-Bey⁵, F. Rousseau^1,4, S. Brochard^1,2,3,6

¹Laboratoire de Traitement de l’Information Médicale INSERM U1101, France; ²Université de Bretagne Occidentale, Brest, France; ³CHU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, France; ⁴IMT Atlantique, LaTIM U1101 INSERM, France; ⁵IMT Atlantique, UMR CNRS 6285, Lab-STICC, Brest, France; ⁶Fondation Ildys, France

PHYSICS-INFORMED MACHINE LEARNING FOR ENHANCED 4D FLOW

V. Lannelongue, P. Garnier, A. Larcher, P. Meliga, E. Hachem

Mines Paris PSL - CEMEF, France