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Objective: Exposure to coal dust can cause interstitial lung disease (ILD), but whether this is due to pure coal or to the contents of quartz in coal is less clear. Here, we systematically reviewed the relation between ‘pure coal’ and ILD.Methods: In a systematic review based on PRISMA criteria 2945 articles were identified. Strict eligibility criteria, which evaluated the ‘pure coal effect’, led to the inclusion of only nine studies.Results: Among these nine studies six studies indicated an independent effect of the non-quartz part of coal on the development and progression of ILD, two did not demonstrate an effect and one was inconclusive.Conclusions: Although an independent effect of non-quartz coal dust on the development of ILD is supported, due to methodological limitations the evidence is limited and further evidence is needed.

ObjectivesThe natural history of coal workers’ pneumoconiosis (CWP) after cessation of exposure remains poorly understood.MethodsWe characterised the development of and progression to radiographic progressive massive fibrosis (PMF) among former US coal miners who applied for US federal benefits at least two times between 1 January 2000 and 31 December 2013. International Labour Office classifications of chest radiographs (CXRs) were used to determine initial and subsequent disease severity. Multivariable logistic regression models were used to identify major predictors of disease progression.ResultsA total of 3351 former miners applying for benefits without evidence of PMF at the time of their initial evaluation had subsequent CXRs. On average, these miners were 59.7 years of age and had 22 years of coal mine employment. At the time of their first CXR, 46.7% of miners had evidence of simple CWP. At the time of their last CXR, 111 miners (3.3%) had radiographic evidence of PMF. Nearly half of all miners who progressed to PMF did so in 5 years or less. Main predictors of progression included younger age and severity of simple CWP at the time of initial CXR.ConclusionsThis study provides further evidence that radiographic CWP may develop and/or progress absent further exposure, even among miners with no evidence of radiographic pneumoconiosis after leaving the industry. Former miners should undergo regular medical surveillance because of the risk for disease progression.

Background The opencast coal mine is a specific mine differing from the underground mine. There are differences in the way into the ore body, the organization of production, transport technology and other aspects. This study aimed to describe the prevalence of CWP among ex-dust miners in opencast coal mines and estimate the incidence trend of CWP by APC model in the future. Methods All opencast miners who had been exposed to dust for at least 1 year in opencast mines were enrolled in this study. The database included demographic details, occupational history records with the date of dust exposure, physical examination records and pneumoconiosis diagnosis records. An age-period-cohort (APC) model has been carried out in order to explore the effects of the age, period and cohort on the prevalence of CWP among ex-dust opencast miners. Results 8191 opencast miners were enrolled in the study, including 259 miners with CWP and 7932 miners without CWP. The incidence density of CWP would have an increasing trend in opencast mines from 2005 to 2024. The number of possible CWP patients predicted in this period was approximately 492. Of them, 275 miners could have suffered from CWP in 2005–2014 and 217 miners would suffer from CWP in 2015–2024 among the ex-dust opencast miners. Conclusions The APC model had a goodness of fit in predicting the incidence trend of CWP in opencast coal mines. By this model, we predicted that 492 opencast miners could be diagnosed as CWP from 2005 to 2024. Therefore ex-dust opencast miners cannot be ignored and they should have regular physical examinations and detection for CWP.

Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed.

Exposure to dust from the mining environment has historically resulted in epidemic levels of mortality and morbidity from pneumoconiotic diseases such as silicosis, coal workers’ pneumoconiosis (CWP), and asbestosis. Studies have shown that CWP remains a critical issue at collieries across the globe, with some countries facing resurgent patterns of the disease and additional pathologies from long-term exposure. Compliance measures to reduce dust exposure rely primarily on the assumption that all “fine” particles are equally toxic irrespective of source or chemical composition. For several ore types, but more specifically coal, such an assumption is not practical due to the complex and highly variable nature of the material. Additionally, several studies have identified possible mechanisms of pathogenesis from the minerals and deleterious metals in coal. The purpose of this review was to provide a reassessment of the perspectives and strategies used to evaluate the pneumoconiotic potency of coal mine dust. Emphasis is on the physicochemical characteristics of coal mine dust such as mineralogy/mineral chemistry, particle shape, size, specific surface area, and free surface area—all of which have been highlighted as contributing factors to the expression of pro-inflammatory responses in the lung. The review also highlights the potential opportunity for more holistic risk characterisation strategies for coal mine dust, which consider the mineralogical and physicochemical aspects of the dust as variables relevant to the current proposed mechanisms for CWP pathogenesis.

Information on the changes of pulmonary function and the right cardiac structure in patients with coal worker's pneumoconiosis in China is very scarce. This study was performed to clarify the changes of pulmonary function and right cardiac structure in patients with coal worker's pneumoconiosis in China. Pulmonary function, pulmonary artery systolic pressure, and the right cardiac structure were evaluated by spirometry and color Doppler echocardiography. The pulmonary artery systolic pressure of patients with coal worker's pneumoconiosis was increased with disease severity. Patients with coal worker's pneumoconiosis also exhibited an impaired pulmonary function and altered right cardiac structure compared with control subjects. A significant linear correlation of the variables of pulmonary ventilation and diffusion function with the indicators of the right cardiac structure was found in patients with coal worker's pneumoconiosis in China. This study elucidated a deterioration of pulmonary function and right cardiac structure in patients with coal worker's pneumoconiosis in China.

Purpose This paper aims to develop a successful deep learning model with data augmentation technique to discover the clinical uniqueness of chest X-ray imaging features of coal workers' pneumoconiosis (CWP). Patients and methods We enrolled 149 CWP patients and 68 dust-exposure workers for a prospective cohort observational study between August 2021 and December 2021 at First Hospital of Shanxi Medical University. Two hundred seventeen chest X-ray images were collected for this study, obtaining reliable diagnostic results through the radiologists' team, and confirming clinical imaging features. We segmented regions of interest with diagnosis reports, then classified them into three categories. To identify these clinical features, we developed a deep learning model (ShuffleNet V2-ECA Net) with data augmentation through performances of different deep learning models by assessment with Receiver Operation Characteristics (ROC) curve and area under the curve (AUC), accuracy (ACC), and Loss curves. Results We selected the ShuffleNet V2-ECA Net as the optimal model. The average AUC of this model was 0.98, and all classifications of clinical imaging features had an AUC above 0.95. Conclusion We performed a study on a small dataset to classify the chest X-ray clinical imaging features of pneumoconiosis using a deep learning technique. A deep learning model of ShuffleNet V2 and ECA-Net was successfully constructed using data augmentation, which achieved an average accuracy of 98%. This method uncovered the uniqueness of the chest X-ray imaging features of CWP, thus supplying additional reference material for clinical application.

Objectives This study aims to integrate CT imaging with occupational health surveillance data to construct a multimodal model for preclinical CWP identification and individualized risk evaluation. Methods CT images and occupational health surveillance data were retrospectively collected from 874 coal workers, including 228 Stage I and 4 Stage II pneumoconiosis patients, along with 600 healthy and 42 subcategory 0/1 coal workers. First, the YOLOX was employed for automated 3D lung extraction to extract radiomics features. Second, two feature selection algorithms were applied to select critical features from both CT radiomics and occupational health data. Third, three distinct feature sets were constructed for model training: CT radiomics features, occupational health data, and their multimodal integration. Finally, five machine learning models were implemented to predict the preclinical stage of CWP. The model’s performance was evaluated using the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity. SHapley Additive exPlanation (SHAP) values were calculated to determine the prediction role of each feature in the model with the highest predictive performance. Results The YOLOX-based lung extraction demonstrated robust performance, achieving an Average Precision (AP) of 0.98. 8 CT radiomic features and 4 occupational health surveillance data were selected for the multimodal model. The optimal occupational health surveillance feature subset comprised the Length of service. Among 5 machine learning algorithms evaluated, the Decision Tree-based multimodal model showed superior predictive capacity on the test set of 142 samples, with an AUC of 0.94 (95% CI 0.88–0.99), accuracy 0.95, specificity 1.00, and Youden's index 0.83. SHAP analysis indicated that Total Protein Results, original shape Flatness, diagnostics Image original Mean were the most influential contributors. Conclusions Our study demonstrated that the multimodal model demonstrated strong predictive capability for the preclinical stage of CWP by integrating CT radiomic features with occupational health data. Graphical Abstract

Background Coal workers’ pneumoconiosis (CWP) remains one of the most severe occupational diseases in China. Despite the implementation of CWP comprehensive preventive measures, the unreasonable allocation of investment by coal enterprises limits the effect of preventing CWP, especially when the health resources are inadequate. This study aims to evaluate the cost-effectiveness of comprehensive measures for CWP from the perspective of coal enterprises. Methods Comprehensive measures and two primary interventions (engineering controls and individual protective equipment) were selected. A time-dependent Markov model was developed to evaluate cost and quality-adjusted life-years (QALYs). The input data were collected from the survey and literature. A hypothetical null situation, in which the currently implemented interventions were eliminated, was used as a comparator based on the generalised cost-effectiveness analysis (GCEA) recommended by the World Health Organization (WHO). The primary outcomes of the model were reported in terms of incremental cost-effectiveness ratios (ICERs). Uncertainty was verified using one-way and probabilistic sensitivity analyses. Results The QALYs of the comprehensive measures, engineering controls, and individual protective equipment were 17.60, 17.50, and 16.85 years, respectively. Compared with null, the ICERs of the interventions were 65,044.73, 30,865.15, and 86,952.41 RMB/QALY, respectively. Individual protective equipment was dominated by an ICER of -11,416.02 RMB/QALY compared to engineering controls. Sensitivity analysis suggested the robustness of the results. Conclusions The comprehensive preventive measures for CWP that are currently implemented in Chinese state-owned mines are cost-effective. In comprehensive measures, engineering controls are more cost-effective than individual protective equipment. Investment in engineering controls should be increased to improve the cost-effectiveness of preventing CWP.

There has been a resurgence of progressive massive fibrosis (PMF) in the United States, particularly among central Appalachian miners. We characterized the proportion of PMF among former U.S. coal miners applying for Federal Black Lung Program benefits, 1970-2016. Data from the U.S. Department of Labor were used to characterize trends in proportion of PMF cases, defined as an approved black lung claim with a determination of PMF, among all miners who filed for federal benefits between January 1, 1970, and December 31, 2016. Joinpoint, logistic, and linear regression models were used to identify changes in the proportion of claimants with PMF over time. There were 4,679 unique PMF cases among claimants for federal black lung benefits between 1970 and 2016, with 2,474 miners determined to have PMF since 1996. The number of PMF cases among Federal Black Lung Program claimants fell from 404 (0.5% of claimants) in 1978 to a low of 18 cases (0.6%) in 1988, and then increased to 353 cases (8.3%) in 2014. The proportion of federal black lung benefits claimants with PMF has been increasing since 1978 (0.06% annual percent change [APC]; 95% confidence interval [CI], 0.05-0.07%; P < 0.0001), and began increasing at a significantly increased rate after 1996 (0.26% APC; 95% CI, 0.25-0.28%; P < 0.0001). Most miners with PMF (84%) last mined in West Virginia, Kentucky, Pennsylvania, or Virginia. Since 1970, the proportion of claimants with PMF has increased significantly among miners who last worked in Kentucky (16.6% APC; 95% CI, 16.5-16.7%), Pennsylvania (4.7% APC; 95% CI, 4.6-4.8%), Tennessee (16.1% APC; 95% CI, 15.7-16.4%), West Virginia (16.8% APC; 95% CI, 16.6-16.9%), and most sharply among miners last working in Virginia (31.5% APC; 95% CI, 31.2-31.7%), where in 2009, more than 17% of claimants received a PMF determination. The proportion of PMF determinations for the rest of the United States has not exceeded 4%. There has been a resurgence of PMF, particularly in central Appalachian miners. The resurgence of this preventable disease points to the need for improved primary and secondary prevention of dust-related lung disease in U.S. coal miners.