Overview and session details of the ESB2023 congress.
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Session Overview |
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AI / Data-driven modelling in biomechanics IV: Cardiovascular System
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Presentations | ||||||||||
17:00 - 17:12
ID: 645 DATA-DRIVEN FSI SIMULATION OF VENTRICLE AND AORTA INTEGRATING IN VIVO AND IN SILICO DATA 1BioCardioLab, Fondazione Toscana Gabriele Monasterio, Italy; 2University of Rome “Tor Vergata”, Italy; 3University of Twente, The Netherlands; 4Clinical Imaging Department, Fondazione Toscana Gabriele Monasterio, Italy; 5GSSI (Gran Sasso Science Institute), L’Aquila, Italy
17:12 - 17:24
ID: 465 A CT-BASED DEEP LEARNING SYSTEM FOR AUTOMATIC ASSESSMENT OF AORTIC ROOT MORPHOLOGY FOR TAVI PLANNING 1Politecnico di Milano, Italy; 2IRCCS Policlinico San Donato, Italy
17:24 - 17:36
ID: 716 DATA-DRIVEN METHODS FOR PATIENT-SPECIFIC REDUCED ORDER MODELING OF COMPLEX AORTIC FLOWS 1Department of Mechanical Engineering, University College London, London, UK; 2Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
17:36 - 17:48
ID: 853 AORTIC SEGMENTATION VIA SYNTHETIC DATA AUGMENTATION STRATEGY FROM PC-MRI SMALL DATASET 1BioCardioLab - Fondazione Toscana Gabriele Monasterio, Italy; 2Department of Information Engineering, University of Pisa, Italy; 3Department of Industrial Engineering, University of Rome "Tor Vergata", Italy; 4Clinical Imaging Department, Fondazione Toscana Gabriele Monasterio, Italy
17:48 - 18:00
ID: 898 DEEP RESIDUAL AMBIVALENT GRAPH CONVOLUTIONAL NETWORKS FOR BIOMARKER PREDICTION IN LARGE VESSEL NETWORKS 1Swansea University, United Kingdom; 2United Kingdom Atom Energy Authority, Culham Centre for Fusion Energy
18:00 - 18:12
ID: 653 IMPLEMENTING DIGITAL TWINS OF THE CARDIOVASCULAR SYSTEM IN CLINICAL SETTINGS: AN AUTOMATED DEEP LEARNING PIPELINE 1BioCardioLab, Fondazione Toscana Gabriele Monasterio, Italy; 2University of Rome “Tor Vergata”, Italy; 3Department of Information Engineering, University of Pisa, Italy; 4Clinical Imaging Department, Fondazione Toscana Gabriele Monasterio, Italy
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