  Electroanatomical mapping is a technique used in cardiology to create a
detailed 3D map of the electrical activity in the heart. It is useful for
diagnosis, treatment planning and real time guidance in cardiac ablation
procedures to treat arrhythmias like atrial fibrillation. A probabilistic
machine learning model trained on a library of CT/MRI scans of the heart can be
used during electroanatomical mapping to generate a patient-specific 3D model
of the chamber being mapped. The use of probabilistic machine learning models
under a Bayesian framework provides a way to quantify uncertainty in results
and provide a natural framework of interpretability of the model. Here we
introduce a Bayesian approach to surface reconstruction of cardiac chamber
models from a sparse 3D point cloud data acquired during electroanatomical
mapping. We show how probabilistic graphical models trained on segmented CT/MRI
data can be used to generate cardiac chamber models from few acquired locations
thereby reducing procedure time and x-ray exposure. We show how they provide
insight into what the neural network learns from the segmented CT/MRI images
used to train the network, which provides explainability to the resulting
cardiac chamber models generated by the model.
