Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms.

ABSTRACT:

Background

There is increasing evidence that 12-lead electrocardiograms (ECG) can be used to predict biological age, which is associated with cardiovascular events. However, the utility of artificial intelligence (AI)-predicted age using ECGs remains unclear.

Methods

Using a single-center database, we developed an AI-enabled ECG using 17 042 sinus rhythm ECGs (SR-ECG) to predict chronological age (CA) with a convolutional neural network that yields AI-predicted age. Using the 5-fold cross validation method, AI-predicted age deriving from the test dataset was yielded for all ECGs. The incidence by AgeDiff and the areas under the curve by receiver operating characteristic curve with AI-predicted age for cardiovascular events were analyzed.

Results

During the mean follow

SUBMITTER: Hirota N 

PROVIDER: S-EPMC9841236 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature