Background
Patients with moderate to severe aortic stenosis (AS) have increased mortality even when asymptomatic. We hypothesized, that artificial intelligence – (AI) enabled electrocardiogram (ECG) – an inexpensive, ubiquitous, 10 second test – could detect patients with moderate/severe AS.
Methods
263,570 Patients with an echocardiogram (Echo) and ECG performed within 180 days of each other were included. Patients with past cardiac surgery, or pacemaker implantation prior to the echo were excluded. We trained a convolutional neural network (CNN) to detect AS of at least moderate severity (aortic valve velocity ≥ 3 m/sec and/or area ≤ 1.5 cm2), using a 12 lead ECG. The model that achieved the highest AUC on an internal validation set was selected, and the final performance was assessed on third independent dataset.
Results
Of the total cohort, 50% were used for training, 10% for internal validation and 40% for testing the network. Of 105,461 testing patients, 5,088 (4.8%) patients had at least moderate AS and were labeled as “positive”. The area under the receiver operating characteristic curve of the classifier was 0.85 (Fig. 1A). The overall sensitivity, specificity and accuracy were 78%, 75% and 75%, respectively. The predicted probabilities for moderate to severe AS by AI track well with AS progression determined by Echo (Fig. 1B).
Conclusion
Adding AI to the 12 lead ECG can permit early detection of AS. The ECG may serve as a powerful tool to screen for asymptomatic aortic stenosis in the community.