Pioneer showcases future of precision cardiology
Digital twin technology can help find ‘the right treatment for the right patient at the right time.
A virtual replica of a patient’s heart could help physicians predict treatment outcomes before an invasive procedure even begins. Researchers are bringing this technology, known as a cardiac digital twin, from the laboratory to clinical practice, opening new possibilities for personalized treatment of ventricular tachycardia (VT).
The science, described this year in the New England Journal of Medicine, is part of the Friday session “Bioengineering the Gap from Basic Biology to Cardiovascular Therapy.”
Natalia Trayanova, PhD, will discuss “Heart Digital Twins and AI: Engineering Personalized Roadmaps for Therapy.” Her research team develops patient-specific heart digital twins to guide the treatment of heart rhythm disorders.
“These virtual reconstructions are built from data that comes from only that patient,” said Trayanova, who holds the Murray B. Sachs professorship in biomedical engineering and is a professor of medicine at Johns Hopkins University in Baltimore.
Digital twin technology combines decades of physiological research with advanced computational modeling, she said, to create the highly detailed representations of each heart.
Digital twins have predictive value, Trayanova said. “That point is important; otherwise, it's just a computational model. With predictive value, it can guide decisions for precision medicine, which finds the right treatment for the right patient at the right time.”
In the trial, heart digital twins were used to guide VT ablation. "We were the first ever to use digital twins in a clinical trial," Trayanova said, noting that the research was conducted under a Food and Drug Administration investigational device exemption and showed that digital twin-guided ablation is safe and feasible.
With their development based on information from individual hearts, digital twins stand in contrast to traditional artificial intelligence systems that identify patterns across large datasets. This distinction is critical, Trayanova said. The digital twin technology incorporates knowledge across multiple levels of biological organization, from cellular behavior and cell-to-cell interactions to whole-organ function. By combining patient-specific imaging and clinical data with computational physiology, researchers can simulate disease processes and treatment responses before interventions occur, she said.
Much of the team's current work focuses on cardiac arrhythmias. These conditions can be dangerous and often are difficult to treat because every patient's disease presents differently, she said. Digital twin models give physicians a noninvasive tool to better understand an individual's condition and determine the most effective treatment strategy.
In addition to the digital twin innovation, Trayanova’s team is pursuing multimodal AI approaches designed to predict patient risk for a range of cardiovascular conditions, including sudden cardiac death. Although AI and digital twins are fundamentally different technologies, Trayanova said AI can be used to accelerate digital twin simulations and enhance analytical capabilities.
Bioengineering the Gap From Basic Biology to Cardiovascular Therapy: BCVS Symposium Session 4
3-4:15 p.m. | Friday, Nov. 6 | S102A
