Machine Learning in Cardiovascular Biomechanics
Function, Diagnosis, and Prediction
- 1st Edition - June 1, 2027
- Latest edition
- Editors: Reza Avazmohammadi, Michael Sacks
- Language: English
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 litera… Read more
Description
Description
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.
Key features
Key features
- Presents key insights from top engineers, scientists, and clinicians on machine learning models as reference for different cardiovascular applications
- Serves as a comprehensive reference for benchmark modeling techniques, enabling researchers to transition from model selection and comparison to practical application of biomechanical machine learning models
- Includes an examination of whether the machine learning methodologies align with the fundamental physiological principles, providing critical insights into the compatibility between computational techniques and biological systems
Readership
Readership
Biomedical engineers and scientists in the biomedical industry, postdoctoral researchers, and graduate students, all of whom intend to make use of machine learning to model and analyze cardiovascular biomechanics
Table of contents
Table of contents
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
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: June 1, 2027
- Language: English
About the editors
About the editors
Reza Avazmohammadi
Editor
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.
Affiliations and expertise
Assistant professor and the director of the Computational Cardiovascular Bioengineering Laboratory in the Department of Biomedical Engineering at Texas A&M UniversityMichael Sacks
Editor
Professor Sacks is a world authority on cardiovascular modelling and simulation, particularly on developing patient-specific, simulation-based approaches for the understanding and treatment of heart and heart valve diseases. Dr. Sacks was technical editor of the Journal of Biomechanical Engineering, an inaugural fellow of the Biomedical Engineering Society, a fellow of the American Society of Mechanical Engineers, and a fellow of the American Institute for Medical and Biological Engineering. He has received several awards, including the Van C. Mow Medal from the ASME Bioengineering Division, the 2008 Chancellor’s Distinguished Research Award of the University of Pittsburgh, the 2008 Richard Skalak Distinguished Lectureship from Columbia University, and the 2008 SKT Lectureship from the City College of New York. In December 2006, Sacks was selected as one of the “Scientific American” 50 leaders in science and technology.
Affiliations and expertise
Oden Institute for Computational Engineering & Sciences, University of Texas at Austin, USA