Semi-supervised Ensemble Learning for Automatic Interpretation of Lung Ultrasound Videos.

ABSTRACT: Point-of-care ultrasound (POCUS) stands as a safe, portable, and cost-effective imaging modality for swift bedside patient examinations. Specifically, lung ultrasonography (LUS) has proven useful in evaluating both acute and chronic pulmonary conditions. Despite its clinical value, automatic LUS interpretation remains relatively unexplored, particularly in multi-label contexts. This work proposes a novel deep learning (DL) framework tailored for interpreting lung POCUS videos, whose outputs are the finding(s) present in these videos (such as A-lines, B-lines, or consolidations). The pipeline, based on a residual (2+1)D architecture, initiates with a pre-processing routine for video masking and standardisation, and employs a semi-supervised approach to harness available unlabeled data. Addi

SUBMITTER: Malainho B 

PROVIDER: S-EPMC12572431 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature