Penn State Researchers Develop Digital Twins to Improve Heart Disease Treatment

Hui Yang and team create personalized digital models to enable more accurate, customized procedures for patients with heart conditions like atrial fibrillation.

Published on Mar. 4, 2026

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Researchers at Penn State, led by Hui Yang, are developing digital twin models of the human heart to help clinicians better understand the specific needs of each patient and optimize treatment strategies through simulation. The digital twins replicate both the electrical and mechanical functions of the heart, allowing doctors to plan procedures and test approaches before operating on real patients. This work aims to improve outcomes for those with heart disease, the leading cause of death in the U.S.

Why it matters

Current medical training and treatment approaches often rely on generic heart models from cadavers or animals, which do not account for the unique variations in each patient's heart structure and function. The digital twin technology being developed at Penn State could lead to more personalized, effective treatments for heart conditions like atrial fibrillation, reducing surgical risks and improving patient outcomes.

The details

The research team, which includes collaborators from the University of Tennessee and the James A. Haley Veterans' Hospital, is building 3D digital replicas of patients' hearts that model both the electrical conduction and mechanical contraction. This allows clinicians to plan, experiment, and optimize treatment strategies through simulation before operating on real patients. The digital twins are created using CT or MRI scans of a patient's heart, capturing its specific geometry and function.

  • The 5-year, $1.2 million grant from the National Institutes of Health funding this research began in 2021.
  • The team recently published three related papers in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science.

The players

Hui Yang

Gary and Sheila Bello Chair in Industrial and Manufacturing Engineering at Penn State, director of the Complex Systems Monitoring, Modeling, and Controls Lab, and the lead researcher on this project.

Bing Yao

Dan Doulet Early Career Assistant Professor at the University of Tennessee, who started working on this project as a doctoral student in Yang's lab at Penn State.

Runsang Liu

Doctoral student in Yang's lab, working on developing a 3D model of the whole heart that can map electrical signals and predict mechanical contraction.

Timothy Kuo

Doctoral student on the research team, exploring how to use patient data to build personalized digital twin hearts.

Christopher J. DeFlitch

Penn State Professor of Emergency Medicine, collaborator on the project.

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What they’re saying

“We are building digital twin models of a dynamic heart including both electrical conductions and mechanical contractions, so we can plan, experiment and optimize treatment strategies through simulation.”

— Hui Yang, Gary and Sheila Bello Chair in Industrial and Manufacturing Engineering at Penn State (Mirage News)

“The virtual heart provides an unprecedented opportunity to study disease mechanisms and test alternative treatment approaches.”

— Hui Yang, Gary and Sheila Bello Chair in Industrial and Manufacturing Engineering at Penn State (Mirage News)

“Now, we can map the electrical signals, also known as EKG signal, on the body surface to predict the conduction and propagation of electrical waves on the heart. As a result, mechanical contraction will follow the excitation from electrical energy.”

— Runsang Liu, Doctoral student in Yang's lab (Mirage News)

What’s next

The research team plans to continue refining the digital twin technology, with the goal of making it a widely adopted tool to improve heart disease treatment planning and outcomes.

The takeaway

This innovative digital twin approach to modeling the human heart could revolutionize how clinicians prepare for and perform procedures, leading to more personalized, effective care for patients with heart disease - the leading cause of death in the U.S.