Comparing Artificial Intelligence-Enabled Electrocardiogram Models in Identifying Left Atrium Enlargement and Long-term Cardiovascular Risk

Abstract

BackgroundThe role of P-wave in identifying left atrial enlargement (LAE) with the use of artificial intelligence (AI)–enabled electrocardiography (ECG) models is unclear. It is also unknown if AI-enabled single-lead ECG could be used as a diagnostic tool for LAE surveillance. We aimed to build AI-enabled P-wave and single-lead ECG models to identify LAE using sinus rhythm (SR) and non-SR ECGs, and compare the prognostic ability of severe LAE, defined as left atrial diameter ≥ 50 mm, assessed by AI-enabled ECG models vs echocardiography. MethodsThis retrospective study used data from 382,594 consecutive adults with paired 12-lead ECG and echocardiography performed within 2 weeks of each other at Chang Gung Memorial Hospital. UNet++ was used for P-wave segmentation. ResNet-18 was used to develop deep convolutional neural network–enabled ECG models for discriminating LAE. External validation was performed with the use of data from 11,753 patients from another hospital. ResultsThe AI-enabled 12-lead ECG model outperformed other ECG models for classifying LAE, but the single-lead ECG models also showed excellent performance at a left atrial diameter cutoff of 50 mm. AI-enabled ECG models had excellent and fair discrimination on LAE using the SR and the non-SR data set, respectively. Severe LAE identified by AI-enabled ECG models was more predictive of future cardiovascular disease than echocardiography; however, the cumulative incidence of new-onset atrial fibrillation and heart failure was higher in patients with echocardiography-severe LAE than with AI-enabled ECG-severe LAE. ConclusionsP-Wave plays a crucial role in discriminating LAE in AI-enabled ECG models. AI-enabled ECG models outperform echocardiography in predicting new-onset cardiovascular diseases associated with severe LAE.