Machine Learning in Cardiovascular Biomechanics

Function, Diagnosis, and Prediction
Erscheinungsjahr 2027
ISBN: 978-0-443-33608-9
Verlag: Elsevier Science

Buch, Englisch, 400 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

Machine Learning in Cardiovascular Biomechanics: Function, Diagnosis, and Prediction serves as a state-of-the-art reference of the methods and models published in the literature to highlight the current difficulties and encourage further exploration of applications of machine learning to cardiovascular biomechanics. This volume is a valuable asset for researchers and students attempting to address problems in cardiovascular mechanics using machine learning techniques as a reference to previously attempted and successful methods.

Autoren/Hrsg.

Avazmohammadi, Reza

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Vorklinische Medizin: Grundlagenfächer
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Section 1. Machine Learning for Modelling Cardiovascular Tissue Biomechanics
1. High Speed Simulation of the 3D Mechanical Response of Cardiac Tissues
2. Machine-Learning-Based Characterization of Cardiac Tissues
3. Biomechanical Models for Cardiovascular Disease Prediction
4. Automated Multiscale Biomechanical Model Discovery for Soft Tissues: Application to the Myocardium
5. Predictive Modelling of Vascular Microstructure via Deep Learning: Insights into fiber orientation, collagen distribution, and cell density
6. Predicting Mechano-Electrical Cardiac Muscle Excitation Patterns for Diagnosis and Treatment

Section 2. Applications of Machine Learning in Modelling Multiscale Cardiac Physiology
7. Machine Learning Methods for the Simulation of Cardiac Electromechanics
8. Neural Network Based Modelling for High-Speed Organ Level Simulations of the Heart over the Physiological Range
9. Image-Based Predictions of the Regional Extent of Myocardial Infarct
10. Enhancing Computational Modelling with Machine Learning: Optimizing Reduced Order Model Parameters for Improved Clinical Implementation
11. Multi-Modality-Multi-Fidelity Modelling of the Heart using Integrated Synthetic-Clinical Data Training
12. Modelling Ventricular-Arterial Hemodynamics: Bridging the Gap Between Predictions and Clinical Application

Section 3. Applications of Machine Learning in Modelling Multiscale Vascular Physiology
13. Advances in Machine Learning Approaches for Simulating Vascular Flow
14. Simulation of Coronary Artery Bypass Grafting: Machine Learning Optimization for Clinical Use
15. Hemodynamic Analysis of Blood Flow in Stenosed Arteries
16. Applications of Reduced Order Models for Blood Flow Analysis
17. Estimating Blood Flow Velocity from Angiography: Clinical Significance
18. Exploring Microvascular Flow with Deep Learning Methods
19. Advancing Clinical Blood Flow Modelling with Machine Learning Techniques
20. Machine Learning Predictions Vessel Wall Biomechanics in Abdominal Aortic Aneurysm

Section 4. Applications of Machine Learning in Modelling Electrophysiology
21. Advanced Signal Processing Techniques for Feature Extraction from Electrophysiological Data
22. Machine Learning Approaches for Arrhythmia Detection and Classification
23. Deep Learning Methods for Stratifying Risk of Sudden Cardiac Death via Electrophysiological Profiles
24. Optical Mapping with Machine Learning for Detailed Electrophysiological Studies
25. Deep Learning Analysis of Real-time ECG Data from Wearable Devices: Improved Risk Stratification

Über Autor(innen)

Avazmohammadi, Reza
Dr. Reza Avazmohammadi (Avaz) is an assistant professor and the director of the Computational Cardiovascular Bioengineering Laboratory in the Department of Biomedical Engineering at Texas A&M University. Dr. Avaz earned his PhD in Mechanical Engineering from the University of Pennsylvania (2014) in constitutive modelling of soft materials, and his postdoctoral research training focused on cardiac biomechanics. Dr. Avaz’s group primary research interest is to integrate medical imaging with cardiac biomechanics and modelling to advance our understanding of myocardial remodelling in structural heart diseases and develop image-based technologies to improve non-invasive diagnosis and prognosis of these diseases.