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Artificial Intelligence for Computational Modeling of the Heart presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient… Read more
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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.
Graduate students as well as researchers in academia or industry, whose area of research is in subject-specific modeling of heart function, biomedical engineering, computational physiology, medical image analysis and artificial intelligence
1. Introduction
2. Multi-scale Models of the Heart for Individualized Simulations
3. Learning Cardiac Anatomy: from Images to Heart Avatar
4. Data-Driven Reduction of Cardiac Models
5. Machine Learning Methods for Robust Parameter Estimation
6. Clinical Applications
7. Conclusion and Perspective
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