TY - JOUR
T1 - 3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease
AU - Wang, Dee Dee
AU - Qian, Zhen
AU - Vukicevic, Marija
AU - Engelhardt, Sandy
AU - Kheradvar, Arash
AU - Zhang, Chuck
AU - Little, Stephen H.
AU - Verjans, Johan
AU - Comaniciu, Dorin
AU - O'Neill, William W.
AU - Vannan, Mani A.
N1 - Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - 3D printing
KW - artificial intelligence
KW - computational modeling
KW - computed tomography
KW - left atrial appendage
KW - structural heart disease
KW - transcatheter aortic valve replacement
KW - transcatheter mitral valve replacement
KW - transesophageal echocardiogram
UR - http://www.scopus.com/inward/record.url?scp=85090490300&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090490300&partnerID=8YFLogxK
U2 - 10.1016/j.jcmg.2019.12.022
DO - 10.1016/j.jcmg.2019.12.022
M3 - Review article
C2 - 32861647
AN - SCOPUS:85090490300
SN - 1936-878X
VL - 14
SP - 41
EP - 60
JO - JACC: Cardiovascular Imaging
JF - JACC: Cardiovascular Imaging
IS - 1
ER -