Real-world application of deep learning for ECG-based prediction of coronary artery disease and revascularization needs.

ABSTRACT:

Aims

Early detection of the need for coronary revascularization and timely intervention may reduce fatal events, but limited screening tools often leads to underdiagnosis. The aim of this study is to use a deep learning model (DLM) that utilizes electrocardiography (ECG) and the eXtreme Gradient Boosting (XGBoost) model to predict risk of coronary revascularization in the general population.

Methods and results

This study included patients with at least one ECG per patient. The development set comprised 113 451 patients for training a DLM. After excluding patients with elevated troponin I levels and those without follow-up records, the internal validation set consisted of 66 680 patients. The external validation was conducted using data from a community hospital. XGBoost pre

SUBMITTER: Chang CH 

PROVIDER: S-EPMC12629643 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature