Comparing Artificial Intelligence-Enabled Electrocardiogram Models in Identifying Left Atrium Enlargement and Long-term Cardiovascular Risk.
Can J Cardiol
; 40(4): 585-594, 2024 Apr.
Article
in En
| MEDLINE
| ID: mdl-38163477
ABSTRACT
BACKGROUND:
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.METHODS:
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.RESULTS:
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.CONCLUSIONS:
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Cardiovascular Diseases
Type of study:
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Adult
/
Humans
Language:
En
Journal:
Can J Cardiol
Journal subject:
CARDIOLOGIA
Year:
2024
Document type:
Article
Affiliation country:
Country of publication: