Multi-modal deep-learning troponin prediction from electrocardiograms and demographic data.

ABSTRACT:

Aims

Electrocardiograms (ECGs) and troponin (Tn) testing are essential tools for the diagnosis and management of cardiac conditions. Prompt diagnosis using these tools can significantly improve patient outcomes.

Methods and results

The objective of this study was to design and create a deep-learning model capable of predicting high-sensitivity troponin (hs-Tn) elevation in patients undergoing chest-pain triage. We developed a novel, multi-modal, externally validated deep-learning model that incorporates ECG data, age, and sex to predict high-sensitivity troponin-T elevation. The dataset used for this study was multi-centre and externally validated, drawing from data collected in two emergency rooms. The study population included all patients presenting to the ER with either

SUBMITTER: Hilgendorf L 

PROVIDER: S-EPMC12853111 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature