Siemens Inventor of the Year Award 2020, Category Talents

It is with a great honor that I received the very prestigious Siemens Inventor of the Year 2020 award along with other Siemens fellows. I am so humbled to be part of Siemens Inventors family, who made so many contributions to the society. This would not have been possible without all the colleagues, mentors and collaborators with whom I had the chance to work with, with whom we strived to realize our vision of a digital twin of the heart, such a potentially disruptive technology. I am so grateful to all of them, this award is for them! Thank you everyone, let us keep pushing the limits of the technology and make digital twins a healthcare reality!

Check out our new book on “AI for Computational Modeling of the Heart”

Our new book 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.

The content of the book is the result of many years of research and hard work from the team, pushing the limit of personalized computational modeling of the heart. So proud of the team! Congratulations to everyone for this achievement! I hope you will enjoy reading the as much as we enjoyed writing it!

New Jersey 2019 Edison Patent Award

In Fall of 2019, my colleagues and I had the honor to win the New Jersey Edison Patent Award for our work on modeling a human heart.

While modeling a heart on a computer sounds complicated, we started simply. First, we used images to model the geometry and anatomy of the heart. We then used patient data on top of data gathered from imaging, such as ultrasounds, electrocardiograms, and magnetic resonance imaging. The result is a realistic computer model of a human heart.

As detailed in the video, our model behaves as close as possible to a real human heart. This allows us to perform virtual therapies to figure out what types of interventions would work best on a real human heart. We could test different settings, different electrode placements, and other simulated interventions to understand how the heart behaves under these different conditions.

We are honored to have been chosen for the Edison Patent Award. A big congratulations to the entire team!