Abstract: ​This seminar presents a research study for personalized heart surgery planning and optimization with integration of advanced materials design, multi-material 3D printing, and machine learning techniques. In this study, a meta-material design approach was first developed to create a mechanical structure that can mimic mechanical behavior of human aortic valves. The tissue-mimicking heart valves were then fabricated using a multi-material 3D printing process. The 3D printed heart valves can be used for pre-surgery planning of heart disease treatment and intervention. The patient-specific heart valves can serve as “virtual patients” which can be used to generate treatment or surgery data for various patients and conditions. In this research, these 3D printed heart valves were used to augment data from relatively small number of available real patients to create more accurate predictive model with machine learning. This model can be used by physicians and surgeons to make more informed decisions for personalized heart surgery planning and optimization. This methodology and its effectiveness were demonstrated through an application case of planning of transcatheter aortic valve replacement (TAVR) surgery.

Biographical Sketch: ​Dr. Chuck Zhang is the Harold E. Smalley Professor at H. Milton Stewart School of Industrial & Systems Engineering of Georgia Institute of Technology. His current research interests include additive manufacturing, cyber-physical systems, and advanced composites/nanocomposites manufacturing and maintenance. Dr. Zhang has managed or conducted numerous research projects sponsored by major federal agencies including National Science Foundation, National Institute of Standards and Technology, Department of Defense, and Department of Veterans Affairs, as well as industrial companies such as ATK, Cummins, Delta Air Lines and Lockheed Martin. He is a fellow of IISE. Dr. Zhang has published over 190 refereed journal articles and 220 conference papers. He also holds 24 U.S. patents.