Abstract 10633: Deep Learning Model Accurately Quantifies LA Volumes on Routine Non-Contrast Chest CTs and Predicts Adverse Outcomes

Abstract

Introduction/Hypothesis: To develop and validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine noncontrast chest CTs and evaluate for outcomes prognosis. Methods: A prototype AI algorithm, with a conditional variational auto-encoder architecture, was initially developed and trained using training data from 1313 patients (paired CTA and non-contrasted exams; “training group”). Left atrium contours from contrasted cases were used to train the model on corresponding non-contrasted dataset. We then retrospectively evaluated 274 patients (mean age 67.9 years, 54.6% male, 57.2% caucasian) who underwent a routine non ECG-gated, noncontrast chest CT for coronary artery calcium or lung cancer screening for validation of the AI model (“validation group”). Outcomes included heart failure (HF) hospitalization and new-onset atrial fibrillation within 5 years. Simple logistic regressions with appropriate univariate statistics were used for modelling outcomes. Results: In the validation group, there was excellent correlation between AI and expert results with a LA volume index (LAVi) intraclass correlation coefficient of 0.950 (0.936-0.960). Bland-Altman plot demonstrated that the AI underestimated LAVi by a mean 5.86 mL/m2. AI-LAVi independently predicted new-onset atrial fibrillation (AUC = 0.855, OR = 1.12, 95% CI 1.08 - 1.18; p<0.0001). AI-LAVi was independently associated with HF hospitalization (AUC = 0.903, OR = 1.08, 95% CI 1.04 - 1.13, p = 0.0009). Conclusion: In this large validation cohort, AI for automated measurement of LA volume on routine noncontrast chest CTs had excellent agreement with manual quantification. AI-LAVi is independently predictive for new-onset atrial fibrillation and HF hospitalization within 5 years.