Patient-Specific Image-Based Computational Modeling in Congenital Heart Disease: A Clinician Perspective

Abstract

Despite major advances in the understanding of congenital heart disease, the clinical decision-making process is still based on consensus opinion of experts, small prospective and retrospective studies, or registries. Furthermore, because the decision process is mainly supported by empirical data from cohorts of patients with similar conditions, it might not reflect the individual subject nor does it allow making predictions on the outcome in response to a variety of therapeutic options. In response to this need, the new paradigm of “predictive personalized medicine” postulates the use of computational tools that integrate patient-specific medical imaging (as well as other measurements) to simulate and quantify physiologic and pathophysiologic function of the cardiovascular system. The ultimate goal is to perform a subject-specific hemodynamic assessment and, when applicable, to predict the outcome of alternative treatment plans for an individual patient. In this article, we review image-based computational modeling in congenital heart disease. We remark that closer interactions between bioengineers and clinicians, and dedicated cross-disciplinary training are crucial to bridge the gap between image-based modeling and daily clinical scenarios.