Academic Press Artificial Intelligence for Computational Modeling of the Heart

Product Description 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. 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 Review Highlights where computational modeling meets artificial intelligence, along with new approaches for the subject-specific modeling of heart function About the Author 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. 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 effic