Artificial Intelligence Effectively Measures Left Atrial Volumes and Function on Multiphasic Cardiac Cta in Patients with Atrial Fibrillation

Introduction: This study was performed to evaluate a novel AI algorithm for fully automated measurement of LA volumes and function using cardiac CTA in patients with atrial fibrillation (AF). Methods: A prototype AI algorithm (AI-LAV), with a conditional variational auto-encoder architecture, was developed and trained using 555 manually annotated ECG-gated, contrast-enhanced cardiac CTs. A total of 79 patients (mean age 63 ± 12 years; 35 with AF and 44 controls) evaluated between 2017 and 2020 were included in this retrospective study to validate the AI model. Images were analyzed by the trained AI algorithm and experienced radiologists. Left atrial volumes were obtained at cardiac end-systole (LAVmax), end-diastole (LAVmin), and pre-atrial contraction (LAVpc), which were then used to obtain LA function indices (total, passive, and active emptying fractions). Statistical analysis was performed. Results: AI was significantly faster than manual measurement of LA volumes and function (4 s vs 10.8 min, respectively). Agreement between the manual and automated methods was good to excellent overall, and there was stronger agreement in AF patients (all ICCs ≥ 0.877; p<0.001) than controls (all ICCs ≥ 0.799; p<0.001). The AI comparably estimated LA volumes and function in AF patients (all within 1.3 mL and 1.7% of the manual measurement), but overestimated volumes and function by clinically negligible amounts in controls (all by ≤ 4.2 ml and ≤ 3.5%). The AI’s ability to distinguish AF patients from controls using the LA volume index was similar to the experts’ (AUC 0.81 vs 0.82, respectively; p = 0.62). Conclusion: In a validation cohort, the novel AI algorithm efficiently performed fully automated multiphasic CTA-based quantification of left atrial volume and function with similar accuracy to expert manual quantification. Figure. Scatter and Bland-Altman plots for LAVmax and LAVmin. Segmentation comparison between expert (manual) and AI on cardiac CTA.