BME Seminar: Computer-aided Personalized Treatment for Cardiovascular Disease, Dr. Zhenglun Wei, University of Massachusetts Lowell

Monday, February 27, 2023
12:00 pm to 1:00 pm
Floor/Room #
GP1002
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a picture of Prof. Wei

 

Dr. Zhenglun Wei, Faculty Candidate

Assistant Professor

University of Massachusetts Lowell

Computer-aided Personalized Treatment for Cardiovascular Disease

Abstract: Cardiovascular disease is the leading cause of death worldwide. In children, congenital heart defects are the number one cause of infant deaths, affecting around 1% of children born in the United States. Fluid mechanics plays an essential role in developing, progressing, and treating congenital heart defects. For the past decade, advanced flow modeling has revealed increasingly detailed information about cardiovascular fluid mechanics in health and disease. This technique has already led to paradigm shifts in clinical practice by assisting personalized treatment that optimizes cardiovascular surgeries and interventions in a personalized manner. This is possibly one of the most striking and exciting examples of the impact of Digital Twins on society.

Personalized treatment is particularly challenging for patients with congenital heart defects, where significant heterogeneity in anatomy, tissue properties, and hemodynamics occurs. Today, my talk will focus on our experience with personalized treatment for single-ventricle abnormality, one of the most severe congenital heart defects. This abnormality occurs when one ventricle of the heart does not develop well and function properly. These patients usually need emergency treatments. Though the treatments offer promising short-term outcomes, patients suffer from multiple long-term complications, including limited exercise capacity, liver diseases, and low quality of life. In this talk, I will present our current efforts in utilizing patient-specific cardiovascular modeling to (1) assist the development of novel surgical techniques, (2) conduct “virtual surgery” to optimize surgical strategies in a patient-specific manner, and (3) examine the effectiveness of using blood pumps to support the circulatory system for single-ventricle patients if all else fails.

Our long-term goal is to develop paradigm-shifting computational and experimental models that can improve diagnosis/prognosis and aid in the personalized treatment and prevention of cardiovascular diseases. The computational models will involve a machine learning technique that demands low-level computational resources (e.g., laptops and tablets) and can be easily deployed to any medical center worldwide. We will make these models available to cardiologists and heart surgeons to diagnose and treat their patients to improve their short-term and long-term outcomes and quality of life.

Biography: Dr. Wei is an assistant professor at the University of Massachusetts Lowell. His research focuses on developing and applying advanced engineering techniques (both computational and experimental) to investigate cardiovascular biomechanics, disease, and treatment. His work produces significant knowledge and data on the cause, accurate diagnosis and prognosis, and safe and effective treatment (surgical technique and medical devices) of related diseases. His work is funded by the National Institute of Health, the American Heart Association, and private foundations. Moreover, Dr. Wei plays an important role in translating the computer-aid personalized treatment of cardiovascular diseases into clinical applications. He assisted clinicians from the United States and worldwide in optimizing surgical and interventional strategies for real-world patients.

Dr. Wei’s research work has resulted in 40 peer-reviewed papers, one book chapter, and 60+ conference proceedings. He currently serves as the Associate Editor for Cardiovascular Engineering and Technology, the newest journal for the Biomedical Engineering Society.

Please contact kharrison@wpi.edu for a zoom link to this event.

Audience(s)

DEPARTMENT(S):

Biomedical Engineering
Contact Person
Kate Harrison

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