Heart disease is the leading cause of death in the United States. Many patients with heart disease - including those suffering from atrial fibrillation (AFib), the most common arrythmia or abnormal heart rhythm in elder populations - require surgical procedures, all of which differ slightly to account for the precise structural and signal variations of each person. Hui Yang, Gary and Sheila Bello Chair in Industrial and Manufacturing Engineering at Penn State, aims to address this issue with virtually engineered digital twins - or digital replicas - of the human heart for more accurate, personalized treatment.
Yang, director of the Complex Systems Monitoring, Modeling, and Controls Lab, is developing the models to produce a perfect digital replica of a patient's heart to enable clinicians to understand the exact needs of each patient.
"Medical students and trainees often practice on hearts from cadavers or animals," Yang said, explaining that such practice builds a foundation of surgical skills but does not help physicians prepare for unexpected variations in practice. "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."
This work is part of a five-year, $1.2 million grant from the National Institutes of Health in collaboration with Bing Yao, Dan Doulet Early Career Assistant Professor at the University of Tennessee. Yao, who earned a dual-title doctoral degree in industrial engineering and operations research from Penn State, started working on this project as a doctoral student in Yang's lab.
Yang's overarching research focus is on optimizing and modeling incredibly complex systems, particularly ones that exhibit nonlinear and chaotic dynamics. In these systems, small changes can cause large and unexpected outcomes, which make it more difficult to predict how they will behave. An example, Yang explained, is the human heart and the process of developing treatments for when it becomes diseased, sustains damage or does not behave in predictable ways.
"It is difficult to conduct the experiments or clinical trials in the human heart," Yang said, pointing to the time and expense of investigating just the potential of therapies before they can be tested for safety and efficacy in more advanced work. "The virtual heart provides an unprecedented opportunity to study disease mechanisms and test alternative treatment approaches."
Yang pointed to AFib as an example. The irregular, often rapid heart rhythm that can lead to blood clots, stroke and heart failure is often treated by a surgical procedure known as catheter ablation. This involves inserting a tube through blood vessels to reach the heart and applying heat, cold or short bursts of energy to the affected area to correct the abnormal rhythms.
During procedures, the heart's response can change quickly, and signals from conventional monitoring techniques - like an EKG - can be difficult to interpret, increasing the potential surgical risks, which can include blood clots, infection or bleeding, tissue damage or stroke. According to Yang, those risks could potentially be minimized if the surgeons can simulate the procedure in a digital replica of each patient's heart to account for personal details, like a faulty valve or weaker cardiac tissues - and the technology is getting closer.
"We are working on developing a whole heart, a spatial-temporal system with evolving dynamics in three-dimensional space," said Runsang Liu, doctoral student in Yang's lab. "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."
By assessing both electrical and mechanical aspects, the researchers aim to use that data to orchestrate the dual functionality of a human heart. Simultaneously, doctoral student and team member Timothy Kuo is actively exploring how to use patient data to build personalized digital twin hearts.
"Everyone has a different heart in terms of geometry and function," Kuo said. "The digital rendering would be based on a CT or MRI. You would scan the heart, and its image would help build a personalized heart model."
The digital twin requires more than just the shape of the heart, though - it also needs to replicate the organ's electrical activity and mechanical contraction. Taken together, Yang said, the digital twin should display the specific physical features of a patient's heart while also modeling how it functions.
Liu and Yang recently published three papers in Chaos: An Interdisciplinary Journal of Nonlinear Science related to their research, and introducing a self-organizing network framework for cardiac systems. Beginning with structural representation, extending to electrical dynamics and culminating in contraction mechanics, this trilogy of work established a coherent nonlinear systems approach to understanding the human heart, according to Yang.
Yang expressed support for his many collaborators, which also include Penn State Professor of Emergency Medicine Christopher J. DeFlitch and Penn State Professor of Medicine Soraya M. Samii, Penn State Professor of Biomedical Engineering Keefe B. Manning, and Fabio Leonelli, a cardiologist from James A. Haley Veterans' Hospital.
In addition to the NIH funding, this research is supported in part by Penn State Center for Health Organization Transformation and in part by the Institute for Computation and Data Sciences, which provides access to the Roar Collab cluster, a high-performance research computing system that supports computation-intensive workloads.
