Recent advancements in deep learning have led to significant improvements in pneumoconiosis diagnosis from chest X-rays (CXR). However, these models typically require large training datasets, which are challenging to collect due to the rarity of the ...
Journal of occupational and environmental medicine
Jan 28, 2025
OBJECTIVE: The aims of the study were to develop and evaluate "eTóraxLaboral," an intelligent platform for detecting signs of pneumoconiosis in chest radiographs and to assess its predictive capacity.
Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, is characterized by chronic pulmonary inflammation and progressive fibrosis, potentially leading to respiratory and/or he...
Lung disease analysis in chest X-rays (CXR) using deep learning presents significant challenges due to the wide variation in lung appearance caused by disease progression and differing X-ray settings. While deep learning models have shown remarkable ...
BACKGROUND: Pneumoconiosis has a significant impact on the quality of patient survival due to its difficult staging diagnosis and poor prognosis. This study aimed to develop a computer-aided diagnostic system for the screening and staging of pneumoco...
Early detection of pneumoconiosis by routine health screening of workers in the mining industry is critical for preventing the progression of this incurable disease. Automated pneumoconiosis classification in chest X-ray images is challenging due to ...
Journal of imaging informatics in medicine
Jun 5, 2024
Accurate prediction of pneumoconiosis is essential for individualized early prevention and treatment. However, the different manifestations and high heterogeneity among radiologists make it difficult to diagnose and stage pneumoconiosis accurately. H...
Accurate and early detection of pneumoconiosis using chest X-rays (CXR) is important for preventing the progression of this incurable disease. It is also a challenging task due to large variations in appearance, size and location of lesions in the lu...
Computer methods and programs in biomedicine
Jan 4, 2024
OBJECTION: The aim of this study is to develop an early-warning model for identifying high-risk populations of pneumoconiosis by combining lung 3D images and radiomics lung texture features.
BACKGROUND: This study aims to explore appropriate model for predicting the disease burden of pneumoconiosis in Tianjin by comparing the prediction effects of Autoregressive Integrated Moving Average (ARIMA) model, Deep Neural Networks (DNN) model an...
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