In an analysis of 69,173 UK Biobank participants, we paired MRI-based measurements of the ascending aortic diameter with ECG signal. We trained a 1D convolutional neural network (ECGAI-TAA) to consume the 10-second 500Hz 12-lead signal and to emit an estimate of the ascending aortic diameter. We assessed model performance in an internal test set of 5,191 participants. The resulting model explained 31% of the variance in aortic diameter, and it couldn't be fully explained by clinical factors such as age, sex, height, weight, pulse rate, blood pressure, or left ventricular mass. Evaluating a clinically relevant diameter threshold (4.0cm, representing dilation), 2.5% of the population had a dilated ascending aorta; when comparing that same proportion of the population (individuals in the top 2.5% of the deep learning model score) to the remaining participants, we found a nearly 16-fold odds ratio for aortic dilation. Using a variational autoencoder-based visualization, we hypothesized that a lateral-superior axis shift may underlie the electrical changes being detected by the model. An important limitation is that these findings represent a physiological observation, not an externally validated risk score. In conclusion, the ECGAI-TAA deep learning model demonstrates that ascending aortic diameter can be, in part, estimated from the 12-lead ECG.
Demarais et al. (Thu,) studied this question.
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