Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography.

Our ML approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF.