AIMC Topic: Pneumoconiosis
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Clinical Validation of an AI System for Pneumoconiosis Detection Using Chest X-rays.
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.
A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.
Frontiers in public health
Jan 10, 2025
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...
Attention-based multi-residual network for lung segmentation in diseased lungs with custom data augmentation.
Scientific reports
Nov 22, 2024
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 ...
Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.
BMC medical imaging
Jul 2, 2024
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...
AMFP-net: Adaptive multi-scale feature pyramid network for diagnosis of pneumoconiosis from chest X-ray images.
Artificial intelligence in medicine
Jun 18, 2024
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 ...
Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists.
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...
DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.
Scientific reports
May 21, 2024
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...
Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis.
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.
Comparison of ARIMA model, DNN model and LSTM model in predicting disease burden of occupational pneumoconiosis in Tianjin, China.
BMC public health
Nov 24, 2022
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...
A Fully Deep Learning Paradigm for Pneumoconiosis Staging on Chest Radiographs.
IEEE journal of biomedical and health informatics
Oct 4, 2022
Pneumoconiosis staging has been a very challenging task, both for certified radiologists and computer-aided detection algorithms. Although deep learning has shown proven advantages in the detection of pneumoconiosis, it remains challenging in pneumoc...
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