We use biophysical simulations across large cohorts combined with machine learning techniques with the aim of predicting long-term response after catheter ablation therapy for atrial fibrillation. For example, we used machine learning classifies to combine patient-specific models of atrial fibrillation, derived metrics of atrial fibrillation physiology, clinical demographics, and imaging data for long-term atrial fibrillation recurrence.
Further reading:
[1] Overview of deep learning pipeline for binary prediction of AF ablation outcome
[2] Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models
[3] Atrial Fibrosis Distribution Generation Based on the Diffusion Models
[4] In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation
[5] Using machine learning to identify local cellular properties that support re-entrant activation in patient-specific models of atrial fibrillation