Robust Personalization of Cardiac Computational Models
Heart failure (HF) is a major cause of morbidity and mortality in the Western world, yet early diagnosis and treatment remain a major challenge. As computational cardiac models are becoming more mature, they are slowly evolving into clinical tools to better stratify HF patients, predict risk and perform treatment planning. A critical prerequisite, however, is their ability to precisely capture an individual patient’s physiology. The process of fitting a model to patient data is called personalization, which is the overarching topic of this thesis.
An image-based, multi-physics 3D whole-heart model is employed in this work. It consists of several components covering anatomy, electrophysiology, biomechanics and hemodynamics. Building upon state-of-the-art personalization techniques, the first goal was to develop an automated pipeline for personalizing all components of the model in a streamlined and reproducible fashion, based on routinely acquired clinical data. Evaluation was performed on a multi-clinic cohort consisting of 113 patients, the largest cohort in any comparable study to date. The goodness of fit between per- sonalized models and ground-truth clinical data was mostly below clinical variability, while a full personalization was finalized within only few hours. This showcases the ability of the proposed pipeline to extract advanced biophysical parameters robustly and efficiently.
Designing such personalization algorithms is a tedious, model- and data-specific process. The second goal was to investigate whether artificial intelligence (AI) con- cepts can be used to learn this task, inspired by how humans manually perform it. A self-taught artificial agent based on reinforcement learning (RL) is proposed, which first learns how the model behaves, then computes an optimal strategy for person- alization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters. The obtained results for two different mod- els suggested that equivalent, if not better goodness of fit than standard methods could be achieved, while being more robust and with faster convergence rate. AI ap- proaches could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.
Due to limited data, uncertainty in the clinical measurements, parameter non- identifiability, and modeling assumptions, various combinations of parameter val- ues may exist that yield the same quality of fit. The third goal of this work was uncertainty quantification (UQ) of the estimated parameters and to ascertain the uniqueness of the found solution. A stochastic method based on Bayesian inference and fast surrogate models is proposed, which estimates the posterior of the model, taking into account uncertainties due to measurement noise. Experiments on the biomechanics model showed that not only goodness of fits equivalent to the standard methods could be achieved, but also the non-uniqueness of the problem could be demonstrated and uncertainty estimates reported, crucial information for subsequent clinical assessments of the personalized models.
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