Inter-machine harmonization of multicenter echocardiographic images for improvement of left ventricular ejection fraction prediction model.

ABSTRACT: One of the common challenges in medical artificial intelligence (AI) applications using echocardiography is the lack of image data harmonization. This study aims to improve the prediction accuracy of left ventricular ejection fraction (LVEF) AI models by incorporating data augmentation (DA) techniques to address the variability in image data across different vendor machines. A database comprising 15,770 echocardiographic videos from 3154 patients across five different centers was utilized, with the data acquired using various vendor machines. We prepared datasets specific to GE healthcare (GE), philips (PH), and canon medical systems (CA) vendors, including 1911, 804, and 427 cohorts, respectively. A three-dimensional convolutional neural network (3D-CNN) was trained to predict LVEF, using

SUBMITTER: Iwasaki R 

PROVIDER: S-EPMC12559306 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature