Wensi Wu uses digital twins to explore the hidden mechanics of the human heart
Wu, a research faculty member at Penn’s School of Engineering and Applied Science, develops ‘digital twins’ of the human heart through computational modeling that capture both the visible and invisible aspects of cardiac function.
2 min. read
“I’ve always been fascinated by how powerful technologies can model what really happens in the world,” says Wensi Wu, research assistant professor in mechanical engineering and applied mechanics. “That curiosity led me to build models of everything from naval vessels to blast impacts on the brain.”
Wensi Wu is a research assistant professor in mechanical engineering and applied mechanics at Penn Engineering. Her current research is focused on improving model fidelity and speed, bridging biomechanical principles with machine learning algorithms to infer tissue properties directly from medical images.
(Image: Courtesy of Penn Engineering)
Wu is a research faculty member at Penn’s School of Engineering and Applied Science, where she’s developing “digital twins” of the human heart. Digital twins are virtual simulations made through computational modeling that provide a highly detailed virtual model of a physical system, object, or process that mirrors its real-world counterpart in both structure and behavior. Unlike a simple simulation, a digital twin is continuously informed by real data, often from sensors, medical images or experimental results, which allows it to evolve and respond dynamically, just like the system it’s replicating. Wu’s work focuses on computational models that capture both the visible and invisible aspects of cardiac function: from fluid dynamics to the microstructure of heart tissue.
“Computational modeling allows you to see the invisible,” she explains. “Medical images can show you shapes in grayscale, but they can’t reveal the hidden forces, the tissue stiffness or the stress on a failing heart valve. With a digital twin, we can.”
In Wu’s lab, digital twins take shape as computational models of the heart that combine imaging data, mechanical properties and simulations of surgical interventions. “We can model what happens during heart valve failure, or simulate surgical procedures like annuloplasty, where a ring is placed on a heart valve to help it close properly,” Wu explains. “Our models help us understand how that intervention affects fluid flow and tissue stress. If the size of the ring is too tight, it could cause damage in the opposite direction.”
Working with collaborators at Penn’s Perelman School of Medicine in the Department of Radiology and clinicians at Children’s Hospital of Philadelphia, Wu combines general data to build foundational models and specific patient data to make predictions with clinical relevance.
“We want to identify patterns that differentiate a working valve from one prone to failure,” she says. “Our ultimate goal is to inform diagnosis, predict drug responses and guide surgical planning in a more personalized way.”
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