Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient’s heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. List of figures - p. ix 6. List of contributors - p. xvii 7. Foreword - p. xix 8. Preface - p. xxiii 9. List of abbreviations - p. xxxiii 10. Part 1: Modeling of the beating heart: approaches and implementation - p. xxxiv 11. 1: Multi-scale models of the heart for patient-specific simulations - p. 3 12. 2: Implementation of a patient-specific cardiac model - p. 43 13. Part 2: Artificial intelligence methods for cardiac modeling - p. 94 14. 3: Learning cardiac anatomy - p. 97 15. 4: Data-driven reduction of cardiac models - p. 117 16. 5: Machine learning methods for robust parameter estimation - p. 161 17. 6: Additional clinical applications - p. 183 18. Bibliography - p. 211 19. Index - p. 235
Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient’s heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. List of figures - p. ix 6. List of contributors - p. xvii 7. Foreword - p. xix 8. Preface - p. xxiii 9. List of abbreviations - p. xxxiii 10. Part 1: Modeling of the beating heart: approaches and implementation - p. xxxiv 11. 1: Multi-scale models of the heart for patient-specific simulations - p. 3 12. 2: Implementation of a patient-specific cardiac model - p. 43 13. Part 2: Artificial intelligence methods for cardiac modeling - p. 94 14. 3: Learning cardiac anatomy - p. 97 15. 4: Data-driven reduction of cardiac models - p. 117 16. 5: Machine learning methods for robust parameter estimation - p. 161 17. 6: Additional clinical applications - p. 183 18. Bibliography - p. 211 19. Index - p. 235
