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

The 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. This 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. The 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. P-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. Le rôle de l’onde P dans la détection de la dilatation auriculaire gauche (DAG) à l’aide des modèles d’électrocardiographie (ECG) basés sur l’intelligence artificielle (IA) est incertain. On ne sait pas non plus si l’EGC à dérivation unique basé sur l’IA pourrait servir d’outil de diagnostic pour la surveillance de la DAG. Nous avions pour objectif de créer des modèles d’ECG à onde P basés sur l’IA et d’ECG à dérivation unique pour détecter la DAG au moyen d’ECG en rythme sinusal (RS) et en non-RS, et de comparer la capacité pronostique de la DAG grave, défini par un diamètre auriculaire gauche ≥ 50 mm, évalué par les modèles d’ECG basé sur l’IA vs l’ECG. La présente étude rétrospective était fondée sur les donn