Subject-specific computational models, also called digital twins, have the potential to provide new ways of understanding a disease, simulating therapies and predicting outcomes. In parallel, the increasing availability of medical data and progress in artificial intelligence (AI) led to significant breakthroughs in various scientific domains. 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 and the compromise that modelers may need to do to achieve subject-specific simulations. The reader will then learn how AI technologies could unlock robust estimation of cardiac anatomy, obtain meta-models for real-time biophysical computations and estimate model parameters from routine clinical data. The concepts introduced in this book will be illustrated through concrete clinical applications.
- 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 estimation of physiological parameters from clinical data
- Illustrates the technology through concrete clinical applications and discusses potential impacts and next steps needed for clinical translation
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
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
- No. of pages:
- © Academic Press 2020
- 1st October 2019
- Academic Press
- Paperback ISBN:
Dr. Tommaso Mansi obtained his undergraduate and M.Sc degrees in Image Processing, Computer Science, and Telecommunications Engineering at Telecom ParisTech, France, and Politecnico di Torino, Italy. Dr. Mansi obtained his Ph.D. in Biomedical Engineering at INRIA Sophia-Antipolis, Ecole des Mines de Paris, France in 2010. Since he joined Siemens Healthcare in 2010, Dr. Mansi has devoted his professional career to researching and developing key innovation for healthcare, specializing in medical image analysis, subject-specific computational modeling applications and artificial intelligence. In particular, Dr. Mansi has contributed to the development of core algorithms of many Siemens Healthineers applications. Dr. Mansi now leads a team of research scientists focusing in image-guided therapy and digital twin technologies, including deep learning, deep reinforcement learning, multi-modality medical image analytics, and patient-specific modeling of organ functions. Dr. Mansi and his team have received several awards, including the 2015 Edison Patent Award in the medical informatics category for the patent Valve Treatment Simulation from Medical Diagnostic Imaging Data, and young scientist awards at the international Medical Image Computing and Computer Assisted Intervention conference for his work in computational modeling of the heart and image interpretation.
Senior Director, Image-Guided Therapy and Digital Twin Research Group, Siemens Healthcare
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 cardiovascular system, including the assessment of the stability and rupture risk of brain aneurysms and aortic abdominal aneurysms. In 2013 Dr. Passerini was awarded a post-doctoral fellowship by the American Heart Association, for independent exploratory research. After joining Siemens Healthcare, Dr. Passerini expanded his areas of interest to the high performance computational modeling of heart electrophysiology and heart electromechanics, including the use of artificial intelligence for the estimation of physical properties of healthy and diseased tissues, and the efficient computation of heart biomechanics., in the context of multiple clinical applications.
AI Scientist, Cardiovascular Research Group, Siemens Healthcare
Dr. Dorin I. Comaniciu obtained his undergraduate and M.Sc degrees in Electronics and Telecommunications at Polytechnic University of Bucharest, Romania. Dr. Comaniciu obtained his Ph.D. in Electrical and Computer Engineering from Rutgers University, USA in 2000. Dr. Comaniciu has devoted his professional career to innovations in cardiovascular imaging, whole body and oncology imaging, interventional imaging, image-guided surgery, and pioneering algorithms and applications in machine learning. Dr. Comaniciu has Siemens research and development in medical imaging and computer vision for 12 years. He is the holder of 216 US and 33 international granted patents. He has expertise in medical imaging, scanner automation, cardiac modeling, whole-body and semantic imaging, biomedical informatics, personalized healthcare, computer vision, machine learning, artificial intelligence, and condition monitoring. He has received numerous awards, including Wikipedia List of Prolific Inventors, List of Most Influential Scholars in Computer Vision, and Siemens Top Innovator, for Knowledge Based Imaging.
Senior Vice President for Artificial Intelligence and Digital Innovation, Siemens Healthcare