A deep multi-stream model for robust prediction of left ventricular ejection fraction in 2D echocardiography.

ABSTRACT: We propose a deep multi-stream model for left ventricular ejection fraction (LVEF) prediction in 2D echocardiographic (2DE) examinations. We use four standard 2DE views as model input, which are automatically selected from the full 2DE examination. The LVEF prediction model processes eight streams of data (images + optical flow) and consists of convolutional neural networks terminated with transformer layers. The model is made robust to missing, misclassified and duplicate views via pre-training, sampling strategies and parameter sharing. The model is trained and evaluated on an existing clinical dataset (12,648 unique examinations) with varying properties in terms of quality, examining physician, and ultrasound system. We report [Formula: see text] and mean absolute error = 4.0% points fo

SUBMITTER: Alven J 

PROVIDER: S-EPMC10808096 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature