Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.

ABSTRACT:

Objective

To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG).

Methods

A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs fr

SUBMITTER: Attia ZI 

PROVIDER: S-EPMC8327278 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature