An explainable artificial intelligence-enabled electrocardiogram analysis model for the classification of reduced left ventricular function.

ABSTRACT:

Aims

The black box nature of artificial intelligence (AI) hinders the development of interpretable AI models that are applicable in clinical practice. We aimed to develop an AI model for classifying patients of reduced left ventricular ejection fraction (LVEF) from 12-lead electrocardiograms (ECG) with the decision-interpretability.

Methods and results

We acquired paired ECG and echocardiography datasets from the central and co-operative institutions. For the central institution dataset, a random forest model was trained to identify patients with reduced LVEF among 29 907 ECGs. Shapley additive explanations were applied to 7196 ECGs. To extract the model's decision criteria, the calculated Shapley additive explanations values were clustered for 192 non-paced rhythm patients

SUBMITTER: Katsushika S 

PROVIDER: S-EPMC10232279 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature