3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease
Dee Dee Wang, Zhen Qian, Marija Vukicevic, Sandy Engelhardt, Arash Kheradvar, Chuck Zhang, Stephen H Little, Johan Verjans, Dorin Comaniciu, William W O'Neill, Mani A Vannan, Dee Dee Wang, Zhen Qian, Marija Vukicevic, Sandy Engelhardt, Arash Kheradvar, Chuck Zhang, Stephen H Little, Johan Verjans, Dorin Comaniciu, William W O'Neill, Mani A Vannan
Abstract
Structural heart disease (SHD) is a new field within cardiovascular medicine. Traditional imaging modalities fall short in supporting the needs of SHD interventions, as they have been constructed around the concept of disease diagnosis. SHD interventions disrupt traditional concepts of imaging in requiring imaging to plan, simulate, and predict intraprocedural outcomes. In transcatheter SHD interventions, the absence of a gold-standard open cavity surgical field deprives physicians of the opportunity for tactile feedback and visual confirmation of cardiac anatomy. Hence, dependency on imaging in periprocedural guidance has led to evolution of a new generation of procedural skillsets, concept of a visual field, and technologies in the periprocedural planning period to accelerate preclinical device development, physician, and patient education. Adaptation of 3-dimensional (3D) printing in clinical care and procedural planning has demonstrated a reduction in early-operator learning curve for transcatheter interventions. Integration of computation modeling to 3D printing has accelerated research and development understanding of fluid mechanics within device testing. Application of 3D printing, computational modeling, and ultimately incorporation of artificial intelligence is changing the landscape of physician training and delivery of patient-centric care. Transcatheter structural heart interventions are requiring in-depth periprocedural understanding of cardiac pathophysiology and device interactions not afforded by traditional imaging metrics.
Keywords: 3D printing; artificial intelligence; computational modeling; computed tomography; left atrial appendage; structural heart disease; transcatheter aortic valve replacement; transcatheter mitral valve replacement; transesophageal echocardiogram.
Conflict of interest statement
Author Disclosures This project was not supported by external funding. Dr. Wang has served as a consultant for Edwards Lifesciences, Highlife Medical, Boston Scientific, and Materialise; and receives research grant support from Boston Scientific assigned to her employer, Henry Ford Health System. 3D Printing at Henry Ford Health System is in part funded via a grant from Ford Motor Co. Fund. Dr. Engelhardt's work is supported by Informatics for Life funded by the Klaus Tschira Foundation and DFG grant EN 1197/2-1. Dr. Little has received research support from Medtronic, Abbott, and Siemens. Dr. Comaniciu is an employee of Siemens Healthineers. Dr. O’Neill has served as a consultant for Edwards Lifesciences, Medtronic, Boston Scientific, Abbott Vascular, and St. Jude Medical; and serves on the Board of Directors of Neovasc Inc. All other authors report they have no relationships relevant to the content of this paper to disclose.
Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
Source: PubMed