[1] Mandrioli D, Schlünssen V, Ádámd B, et al. WHO/ILO work-related burden of disease and injury: protocol for systematic reviews of occupational exposure to dusts and/or fibres and of the effect of occupational exposure to dusts and/or fibres on pneumoconiosis. Environ Int, 2018; 119, 174−85.
[2] International Labour Organization (ILO). Guidelines for the use of the ILO international classification of radiographs of pneumoconioses. ILO. 2011, 3-6.
[3] Ather S, Kadir T, Gleeson F. Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications. Clin Radiol, 2020; 75, 13−9.
[4] Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med, 2018; 15, e1002686.
[5] Singh R, Kalra MK, Nitiwarangkul C, et al. Deep learning in chest radiography: detection of findings and presence of change. PLoS One, 2018; 13, e0204155.
[6] Turner AF, Kruger RP, Thompson WB. Automated computer screening of chest radiographs for pneumoconiosis. Invest Radiol, 1976; 11, 258.
[7] Yu PC, Xu H, Zhu Y, et al. An automatic computer-aided detection scheme for pneumoconiosis on digital chest radiographs. J Digit Imaging, 2011; 24, 382−93.
[8] Zhu L, Zheng R, Jin H, et al. Automatic detection and recognition of silicosis in chest radiograph. BioMed Mater Eng, 2014; 24, 3389−95.
[9] Okumura E, Kawashita I, Ishida T. Development of CAD based on ANN analysis of power spectra for pneumoconiosis in chest radiographs: effect of three new enhancement methods. Radiol Phys Technol, 2014; 7, 217−27.
[10] Okumura E, Kawashita I, Ishida T. Computerized classification of pneumoconiosis on digital chest radiography artificial neural network with three stages. J Digit Imaging, 2017; 30, 413−26.