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McKelvey School of Engineering

Brown School

MEMS Seminar: Automating Image-Based Computational Modeling of Cardiac Mechanics for Personalized Treatment Planning

Thursday, February 15 | 2:30 PM - 3:30 PM

Stephen F. & Camilla T. Brauer Hall, 012
6548 Forest Park Pkwy, St. Louis, MO 63112, USA

Fanwei Kong,  Postdoctoral Scholar, Stanford University.

Abstract: Cardiovascular disease is the leading cause of death worldwide, and current treatments derived from population studies are often not effective for individuals. Image-based computational modeling of cardiac mechanics for individual patients, including electromechanics and fluid dynamics, can enable personalized treatment planning. This approach can also non-invasively review detailed cardiac physiology, providing insights into the biomechanical underpinning of many cardiovascular diseases. However, the time-consuming and labor-intensive process of deriving such digital twin models of patients’ hearts from clinical data has hindered the clinical use of this paradigm.

In this talk, I will outline the use of machine learning to significantly expedite image-based model development and facilitate efficient patient-specific simulations of the heart. I will begin by introducing deep learning algorithms capable of automatically constructing time-series whole-heart meshes within seconds from patient medical image data for simulating cardiac mechanics. Additionally, I will discuss deep learning algorithms facilitating the efficient generation of digital twin models for simulating patient-specific cardiac electrophysiology. I will then present a generative deep-learning approach designed to generate synthetic datasets covering a diverse range of rare congenital heart defects. These datasets can support data-driven analyses of cardiac mechanics under rare abnormal heart anatomies. Finally, I will briefly discuss the ongoing and future directions of extending these machine-learning and computational approaches to enable personalized surgery planning, enhance medical device design, and improve early risk detection for cardiovascular diseases.


Event Type



McKelvey School of Engineering


Science & Technology



Mechanical Engineering & Materials Science
Event Contact

Kyla Kordell, kkyla@wustl.edu

Speaker Information

Fanwei Kong is a postdoctoral scholar working in the Cardiovascular Biomechanics Computation Lab at Stanford University. She is interested in developing advanced algorithms merging machine learning, medical computer vision, and computational modeling to enable personalized virtual intervention planning for cardiovascular diseases. She received her PhD in mechanical engineering from UC Berkeley in 2022, under the supervision of Prof. Shawn Shadden. Prior to that, she received her B.S. in biomedical engineering from Georgia Tech.

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