Artificial Intelligence for Computational Modeling of the Heart

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 efficient computation of heart biomechanics., in the context of multiple clinical applications. Dorin Comaniciu serves as Senior Vice President for Artificial Intelligence and Digital Innovation at Siemens Healthineers. His scientific contributions to computational imaging and machine intelligence have translated into multiple clinical products focused on improving the quality of care, specifically in the fields of diagnostic imaging, image-guided therapy, and personalized medicine. A Top Innovator of Siemens, Dr. Comaniciu is Fellow of IEEE, ACM, Medical Image Computing and Computer Assisted Intervention Society, and American Institute for Medical and Biological Engineering. He is recipient of multiple honors, including an honorary doctorate and the IEEE Longuet-Higgins Prize for fundamental contributions to computer vision. Comaniciu is listed on Wikipedia's list of prolific inventors with 275 granted patents on healthcare technology. He has co-authored 350 peer-reviewed publications in the areas of machine intelligence, medical imaging and personalized medicine, which have received 40,000 citations. He is an advocate for technological innovation that saves and enhances lives, addressing critical issues in global health.

• 1. Introduction2. Multi-scale Models of the Heart for Individualized Simulations3. Learning Cardiac Anatomy: from Images to Heart Avatar4. Data-Driven Reduction of Cardiac Models5. Machine Learning Methods for Robust Parameter Estimation6. Clinical Applications7. Conclusion and Perspective