Unsupervised Machine Learning Exploration of Morphological and Haemodynamic Indices to Predict Thrombus Formation in the Left Atrial Appendage.

Atrial Fibrillation (AF) is the most common cardiac arrhythmia, and it is associated with an increased risk of embolic stroke. It is known that AF-related thrombus formation occurs predominantly in the left atrial appendage (LAA). However, it is still unknown the structural and functional characteristics of the left atria (LA) that promote low velocities and stagnated blood flow, thus a high risk of thrombogenesis. In this work, we investigated morphological and in-silico haemodynamic indices of the LA and LAA with unsupervised machine learning (ML) techniques, to identify the most relevant features that could subsequently be used to generate thrombus prediction models. A fully automatic pipeline was implemented to extract multiple morphological parameters from a 3D mesh of a LA. Morphological parameters were then combined with particle flow parameters from in-silico fluid simulations. Unsupervised multiple kernel learning (MKL) was used for dimensionality reduction, resulting in a latent space positioning patients based on feature similarity. Clustering applied to the MKL output space estimated clusters with different proportion of thrombus cases. The cluster with the highest risk of thrombus formation was characterised by high values of LAA height, tortuosity and ostium perimeter, as well as total number of flow particles in the LAA and low angle between the LAA and the left superior pulmonary vein, proving the usefulness of unsupervised ML techniques to extract knowledge from the data, and early identify AF patients at higher risk of thrombus formation.