Automated left atrial volume measurement using a deep learning model on routine nongated chest CT was an independent predictor of atrial fibrillation (AUC 0.768, age-adjusted RR 2.9).
Cohort (n=1,000)
Does automated measurement of left atrial volume using a deep learning model on routine nongated chest CT predict atrial fibrillation?
Deep learning-based automated measurement of left atrial volume on routine nongated chest CT can independently predict atrial fibrillation.
Effect estimate: RR 2.9
Purpose To test the performance of a deep learning (DL) model in predicting atrial fibrillation (AF) at routine nongated chest CT. Materials and Methods A retrospective derivation cohort (mean age, 64 years; 51% female) consisting of 500 consecutive patients who underwent routine chest CT served as the training set for a DL model that was used to measure left atrial volume. The model was then used to measure atrial size for a separate 500-patient validation cohort (mean age, 61 years; 46% female), in which the AF status was determined by performing a chart review. The performance of automated atrial size as a predictor of AF was evaluated by using a receiver operating characteristic analysis. Results There was good agreement between manual and model-generated segmentation maps by all measures of overlap and surface distance (mean Dice = 0.87, intersection over union = 0.77, Hausdorff distance = 4.36 mm, average symmetric surface distance = 0.96 mm), and agreement was slightly but significantly greater than that between human observers (mean Dice = 0.85 automated vs 0.84 manual; P = .004). Atrial volume was a good predictor of AF in the validation cohort (area under the receiver operating characteristic curve = 0.768) and was an independent predictor of AF, with an age-adjusted relative risk of 2.9. Conclusion Left atrial volume is an independent predictor of the AF status as measured at routine nongated chest CT. Deep learning is a suitable tool for automated measurement. Keywords: Adults, CT, Cardiac, Computer Aided Diagnosis (CAD), Heart, Informatics, Neural Networks, Thorax © RSNA, 2019 See also the commentary by de Roos and Tao in this issue.
Bratt et al. (Sun,) conducted a cohort in Atrial fibrillation (n=1,000). Deep learning model for left atrial volume measurement vs. Manual measurement was evaluated on Prediction of atrial fibrillation (RR 2.9). Automated left atrial volume measurement using a deep learning model on routine nongated chest CT was an independent predictor of atrial fibrillation (AUC 0.768, age-adjusted RR 2.9).