Advancing arrhythmia patient care with digital twins and AI

ABSTRACT:

Precision medicine is envisioned to provide therapy tailored to each patient. The rapidly increasing ability to capture extensive patient data, coupled with machine learning, is a pathway to achieving this vision. A different pathway towards precision medicine is the increasing ability to encode known physics laws and physiology knowledge within mathematical equations and to adapt such models to represent the behavior of a specific patient.

Wouldn’t it be great to have a digital representation of ourselves that allows doctors to simulate our personal medical history and health conditions using relationships learned both from data and from biophysics knowledge? That virtual replica of ourselves would integrate data-driven machine learning and multiscale physics-based modeling to continuously update itself as our health condition changes and more information about our interaction with the environment is acquired. These digital twins would forecast the trajectory of the patient’s disease, estimate risk of adverse events, and predict treatment response so that the potential outcome would inform treatment decision.

This presentation explores the synergies that have been achieved between machine learning and mechanistic physics-based heart models towards enabling precision medicine in cardiology. It showcases how machine learning and multiscale cardiac modeling complement each other in engineering your heart’s health. A highlight is the robust prediction of sudden cardiac death risk in different heart diseases. Another application of the heart digital twin technology is illustrated by the development of a precise treatment for patients suffering from arrhythmias. This application prevents future re-hospitalizations and repeat procedures, shifting the treatment selection from being based on the state of the patient today to optimizing the state of the patient tomorrow.

BIO:

Dr. Trayanova holds the inaugural Murray B. Sachs Professorship in the Department of Biomedical Engineering at Johns Hopkins University. She is also a Professor of Medicine at the Johns Hopkins School of Medicine, and a Professor of Applied Mathematics and Statistics. She envisioned, created, and directs the Alliance for Cardiovascular Diagnostic and Treatment Innovation. She is also the Director for AI Research in Health and Medicine at Johns Hopkins University under the Data Science and AI Institute, where she is responsible for directing efforts across the university in developing and deploying AI applications that advance healthcare delivery and improve patient outcomes. She also directs the Computational Cardiology Laboratory.

Dr. Trayanova is internationally recognized as a leader in personalized multi-scale computational modeling of whole heart electrophysiology and arrhythmias (heart digital twinning). Her research output includes 450 published papers and book chapters. She has published extensively in the most prestigious journals, such as The Lancet, Nature Cardiovascular Medicine, Nature Communications, Nature BME, Science Advances, Science TM, Physiological Reviews, Nature Reviews Cardiology, eLife, and others.

Trayanova’s work has received world-wide recognition, and she is the recipient of numerous honors and awards. She is the recipient of an NIH Director’s Pioneer Award in 2013; in 2019, she was inducted in the Women of Technology International Hall of Fame, an honor conferred only on 5 women each year from around the world. Also in 2019, she received the Distinguished Scientist Award from Heart Rhythm Society. This was followed by the Zipes Distinguished Award by the same society in 2020, and by the Gordon Moe Award by the Cardiac Electrophysiology Society in 2023. In 2025, Trayanova will be the recipient of the Hodgkin-Huxley-Katz Award by the Physiological Society. Trayanova has been named a Fellow of every American and European clinical cardiology society, testifying to her impact in clinical practice. She is also a Fellow of AIMBE, BMES, IAMBE, and IUPS. She has given over 380 invited lectures, majority of them keynotes or plenary lectures. Dr. Trayanova’s work has received widespread media coverage and recognition (see recent article in the Wall Street Journal, and she has also given a TEDx talk. Dr. Trayanova is also the inventor on numerous patents and patent applications filed world-wide. In recognition of her innovation, she was named Fellow of the National Academy of Inventors in 2020.

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