Additional clinical applications - Meister, Felix; Houle, Helene; Nita, Cosmin; Puiu, Andrei; Mihai Itu, Lucian; Rapaka, Saikiran (Artificial Intelligence for Computational Modeling of the Heart, 2020)
This chapter illustrates three clinical applications of the approaches presented so far in this book. The first one refers to a multi-scale, multi-physics cardiac modeling pipeline for cardiac resynchronization therapy (CRT), based solely on pre-operative, non-invasive data. Results on a small cohort of ten patients are presented and discussed. The second application introduces an AI based model for pressure drop computation in coarctation of the aorta (CoA). Since establishing a large enough patient-specific training database for AI model development would be prohibitively expensive and time consuming for this congenital pathology, a methodology for generating purely synthetic CoA anatomical models is described. The ground truth pressure drop values can be computed for the synthetic models using a three-dimensional Computational Fluid Dynamics (CFD) solver. The AI model reduces the mean absolute pressure drop prediction error more than five times compared to a previously published semi-analytical pressure drop model. Finally, the third application addresses the entire cardiovascular system, based on a lumped parameter model (LPM) of whole body circulation. A similar approach to the one employed for the CoA use case is considered, relying on a purely synthetic training database. Both time-independent, e.g. systemic resistance and compliance, and time-dependent quantities, e.g. ventricular pressure, are predicted. We show that the performance of the AI models is statistically similar to that of the LPM, while the inference time is reduced to milliseconds.<br
Keywords: Cardiac resynchronization therapy; Coarctation of the aorta; Whole-body circulation