Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study.

ABSTRACT:

Aims

Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated.

Methods and results

Adult patients at non-cardiology departments who underwent ECG testing as a part of routine clinical care were included in this prospective cohort study. DELTAnet was used to classify 12-lead ECGs into one of the following triage classes: normal, abnormal not acute, subacute, and acute. Performance was compared with

SUBMITTER: van de Leur RR 

PROVIDER: S-EPMC10802816 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature