Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.

ABSTRACT:

Background

Coal workers' pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future.

Methods

All chest high-resolution computed tomography (HRCT) medical images presented in this work were obtained from 217 coal workers' pneumoconiosis (CWP) patients and dust-exposed workers. We segmented regions of interest according to the diagnostic results, which were evaluated by radiologists. These regions were then classified regions into four categories. We employed an efficient de

SUBMITTER: Dong H 

PROVIDER: S-EPMC11773871 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature