Abstract 15312: Automatic Left Atrial Volume and Sphericity Index Calculation From Cardiac CT Using Computer Graphics Imaging and Deep Learning

Abstract

Introduction: Left atrial (LA) volume and sphericity index are important for atrial fibrillation (AF) recurrence prediction after ablation and AF-associated atrial remodeling analysis. However, calculating these indexes from computed tomography (CT) requires manual outlining, which is subjective and difficult to scale. Hypothesis: We hypothesized LA body can be "virtually" dissected from electroanatomic shells using computer graphics imaging (CGI). We further hypothesized that the results of virtual dissection can be used to train a deep learning (DL) model for automatic LA volume and sphericity index calculation. Methods: First, by encoding the anatomic knowledge with geometric modeling, we developed a mathematical method (termed "virtual dissection") based on CGI techniques to isolate the LA body from electroanatomic shells (Fig A). Second, we used virtual dissection to train a DL model to segment CTs in N=20 patient atria (Fig B-C). Finally, we used this pipeline to segment raw CTs in a test cohort of N=100 patients (30.0% women, 66.0±10.3Y; Fig D). Results: In the independent test cohort (N=100), the LA volume and sphericity index were automatically calculated from DL-based segmentation, with the agreement within 4.04 ml (95% CI: -11.48 to 19.55; r=0.98; p < 0.0001; Fig E-F) and 2.75% (95% CI: -5.71 to 11.22; r=0.88; p < 0.0001; Fig G-H), compared to expert annotation. Conclusions: Encoded anatomical knowledge combined with deep learning in small datasets can accurately extract 3D cardiac models from raw CT scans in large populations, yielding automatic calculation of indexes such as volume and sphericity. This computational pipeline may simplify the outcome analysis and prediction using geometric indexes as predictors.