Novel Domain Knowledge Encoding Enables Machine Learning of Rapid, Expert-level Segmentation of Cardiac Computed Tomography

Abstract

Ablation is a common therapeutic procedure for atrial fibrillation (AF), that is guided to specific targets in the heart often after laborious segmentation of computed tomography. Machine learning (ML) can automate such tasks but requires large training datasets. Inspired by natural intelligence, which builds conceptual models to learn without large datasets, we mathematically encoded domain knowledge of atrial geometry to accelerate ML segmentation. In test cohorts (N=160) at 2 institutions, Dice scores were 96.7% (IQR: 95.3% to 97.7%) and 93.5% (IQR: 91.9% to 94.7%) with similar anatomic agreement to experts (r=0.99; p<0.0001). In a prospective study of patients undergoing AF ablation (N=42), our approach reduced segmentation time by 85% (2.28±0.8 vs 15.0±6.9 minutes; p<0.0001), with similar Dice (p=0.07) versus experts. This approach may broaden the availability of AF ablation, and more broadly shows that encoding of domain knowledge may reduce the dependence of ML on large training datasets.