Parameter Estimation for Personalized Cardiac Models via Active Learning.

Personalized cardiac models are crucial intervention tools for a multitude of cardiac health issues. As cardiac simulations become more complex and expensive, machine learning (ML) models demonstrated the potential to enable efficient model personalization and cardiac tissue parameter estimation. A common approach depends on “globally” accurate ML models trained with large simulation data to predict tissue parameters. Such a global ML model is not only expensive to train, but its success also relies on the assumption that real-world data would fall within the range of the training data. We establish a novel active-learning method for cardiac parameter estimation by steering the training of the ML model towards the unknown region of interest in the parameter space.