Enhancing Medical Imaging with Computational Modeling for Aortic Valve Disease Intervention Planning

Cardiovascular complications and death are potential outcomes of aortic valve disease, which is a severe medical condition that also carries a significant economic burden. The study of fluid mechanics is crucial to understanding the development, progression, and treatment of cardiovascular and aortic valve disease. Technological advancements in imaging methods and patient-specific computational modeling have enabled clinicians to gain more detailed information about blood flow patterns in both healthy individuals and those with disease. This information can be used to obtain non-invasive metrics before and after interventions, which can help in selecting appropriate treatments and ultimately improve patient outcomes. Incorporating information about flow physics into the clinical practice can further enhance current medical knowledge. This chapter will focus on the integration of medical imaging with computational modeling, which will allow for faster modeling, improved data accuracy, and earlier detection of cardiovascular and valvular anomalies. The use of machine learning will also be explored as a means of developing patient-specific diagnostic and predictive tools for characterizing and assessing cardiovascular outcomes. The goal of the chapter is to provide an overview of these approaches and their potential to support decision-making during important clinical milestones in the management of aortic valve disease.