Multidisciplinary research at Rochester Institute of Technology—marrying artificial intelligence and biomedicine—is at the heart of a $3 million project aimed at helping cardiac surgeons.
Linwei Wang, associate professor in RIT’s computing and information sciences Ph.D. program, leads a group of researchers and clinicians who are developing computational systems to create individualized 3D imaging of a patient’s heart. This type of model is expected to enable clinicians to study patients noninvasively, improving care for cardiac arrhythmia and other heart diseases.
“Clinicians want to see how arrhythmia affects the heart physically, which unfortunately means using a catheter as opposed to just an EKG or MRI scan,” Wang says. “With our digital imaging, surgeons can spend hours studying a model of the patient’s heart, better preparing them before and after the procedure—all without negatively affecting the patient.”
Funded by the National Institutes of Health, the project’s core is Wang’s work that uses artificial intelligence to discover new and better ways of understanding physics. Using the findings, Wang is integrating data into her models to make imaging that is more accurate than ever, RIT says.
“I’m interested in the bilateral connection between physics and data—in using data to improve physics-based modeling and learning physics from data,” Wang says. “Being able to apply what we learn to helping patients and addressing the challenges that clinicians face is motivating for me.”
Her prowess in the field has not gone unnoticed. Last year, Wang was honored with the Presidential Early Career Award for Scientists and Engineers. The highest honor given by the U.S. government to outstanding scientists and engineers early in their careers, the PECASE recognizes those who show promise for leadership in science and technology.
Wang, who is director of RIT’s Computational Biomedicine Laboratory, values a multidisciplinary approach to solving research problems. It is how she trains her doctoral students and is the cornerstone of this project as well.
She collaborates with experts in patient-specific cardiac modeling and high-performance computing to develop uncertainty quantification techniques. Leveraging advances in active machine learning, these techniques allow for the propagation of uncertainty from the data used to model elements and create predictions, determining the likelihood of certain outcomes.
This will help address the variability in personalized virtual organ models and help remove the major roadblock to widespread adoption of these models in decision support, Wang says.
The interdisciplinary approach goes a step further. Wang’s team is also working with clinicians to integrate physics knowledge into the development of machine learning methods, including learning to disentangle physiological factors of inter-subject variations from clinical data.
This method will help the team to develop a computer tool to provide real-time automatic guidance during the ablation procedure, often used to stop abnormal electrical signals in the heart that cause irregular heartbeats.
Tapping the potential of artificial intelligence and applying it to medicine is a growing field of inquiry. Using AI for medical imaging through 3D models, for example, can help lower exposure to radiation by creating 3D models from X-ray images, reducing the need for computed tomography or magnetic resonance imaging scans.
Wang’s work in Rochester is a response to the need for smart tools that improve diagnosis and patient care.
“Dr. Wang is a pleasure to work with—we are collaborating to find new ways to understand and image the short circuits in the heart which can cause dangerous heart rhythms,” says John Sapp M.D., professor of medicine at Dalhousie University in Nova Scotia, Canada. “If we can identify where the short circuits are, we may be able to find ways to fix them.”
Smriti Jacob is Rochester Beacon managing editor.