Artificial Intelligence for Computational Modeling of the Heart
Coordonnateurs : Mansi Tommaso, Passerini Tiziano, Comaniciu Dorin
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. 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
Dr. Tiziano Passerini obtained his M.Sc degree in Biomedical Engineering from Politecnico di Milano, Italy in 2005, and his Ph.D. in Mathematical Engineering from Politecnico di Milano, Italy in 2009. Biomedical engineering, mathematical engineering, and high performance scientific computing as applied to the computational modeling of human physiology and pathology are the key components of Dr. Passerini’s expertise. During his doctoral studies in Milan and post-doctoral appointment at Emory University he worked on several projects focusing on the image-based, high performance computational modeling of the c
- Presents recent advances in computational modeling of heart function and artificial intelligence technologies for subject-specific applications
- Discusses AI-based technologies for robust anatomical modeling from medical images, data-driven reduction of multi-scale cardiac models, and estimations of physiological parameters from clinical data
- Illustrates the technology through concrete clinical applications and discusses potential impacts and next steps needed for clinical translation
Date de parution : 11-2019
Ouvrage de 274 p.
19x23.3 cm
Thèmes d’Artificial Intelligence for Computational Modeling of... :
Mots-clés :
Artificial intelligence; computational modeling; digital twin; 3D modeling; patient-specific simulations; heart function; clinical imaging; multi-scale modeling; cardiac arrhythmias; cardiovascular diseases; computational physiology; computational cardiology; deep learning; deep reinforcement learning; meta-modeling