Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 191, P. 110153 - 110153

Published: April 18, 2025

Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be significant global health issue, especially with the increasing use of engineered stone (ES) surfaces containing high silica content. Traditional diagnostic methods, dependent on radiological interpretation, have low sensitivity, especially, in early stages disease, and present variability between evaluators. This study explores efficacy deep learning techniques automating screening staging silicosis using chest X-ray images. Utilizing comprehensive dataset, obtained from medical records cohort workers exposed artificial quartz conglomerates, we implemented preprocessing stage for rib-cage segmentation, followed classification state-of-the-art models. The segmentation model exhibited precision, ensuring accurate identification thoracic structures. In phase, our models achieved near-perfect accuracy, ROC AUC values reaching 1.0, effectively distinguishing healthy individuals those silicosis. demonstrated remarkable precision disease. Nevertheless, differentiating simple progressive massive fibrosis, evolved complicated form presented certain difficulties, during transitional period, when assessment can significantly subjective. Notwithstanding these an accuracy around 81% scores nearing 0.93. highlights potential generate clinical decision support tools increase effectiveness diagnosis silicosis, whose detection would allow patient moved away all sources exposure, therefore constituting substantial advancement diagnostics.

Language: Английский

OMSF2: optimizing multi-scale feature fusion learning for pneumoconiosis staging diagnosis through data specificity augmentation

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 11(1)

Published: Dec. 30, 2024

Language: Английский