Machine learning electrocardiography model to differentiate takotsubo syndrome from myocardial infarction.

ABSTRACT:

Aims

Machine learning (ML) algorithms applied to the electrocardiography (ECG) have been successful in several cardiac diagnoses, however, rarely been used for the diagnostics of takotsubo syndrome (TTS). Our aim was to develop ML-based ECG-models to differentiate TTS from patients with myocardial infarction (MI).

Methods and results

Cross-sectional study in Stockholm. A neural network with UNet architecture was trained and validated on 507 TTS cases and 14 978 controls with suspected and verified MI, identified from the Swedish coronary angiography and angioplasty register. Cross-validation was performed. The models were compared with cardiologists using previously proposed ECG criteria. Receiver operating characteristics (ROC) area under the curve (AUC) for discriminating

SUBMITTER: Hakansson FHK 

PROVIDER: S-EPMC12450510 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature