ArticleScientific reports2021
A deep learning-based model for screening and staging pneumoconiosis.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.
- Computer-Aided Diagnosis of Coal Workers' Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review.International journal of environmental research and public health · 2022Pooled it
- A multimodal deep learning framework for precise silicosis detection on radiographic images.Scientific reports · 2026Article
- Artificial Intelligence in Occupational Health Surveillance: Evaluating AI-Assisted ILO Classification of Radiographs of Pneumoconioses.La Medicina del lavoro · 2026Article
- Highly Accurate Occupational Pneumoconiosis Staging via Dark Channel Prior-Inspired Lesion Area Enhancement.Journal of imaging informatics in medicine · 2025Article
- Multimodal Fusion of Chest X-Rays and Blood Biomarkers for Automated Silicosis Staging.Journal of clinical medicine · 2025Article
- Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection.Scientific data · 2025Article
- Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records.Journal of translational medicine · 2025Article
- Impact of Radiologist Experience on AI Annotation Quality in Chest Radiographs: A Comparative Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.Biomedical engineering online · 2025Article
- Integrating CT radiomics and clinical data with machine learning to predict fibrosis progression in coalworker pneumoconiosis.Frontiers in medicine · 2025Article
- Development of a machine learning model in prediction of the rapid progression of interstitial lung disease in patients with idiopathic inflammatory myopathy.Quantitative imaging in medicine and surgery · 2024Article
- The role of pathologists in the diagnosis of occupational lung diseases: an expert opinion of the European Society of Pathology Pulmonary Pathology Working Group.Virchows Archiv : an international journal of pathology · 2024Review
- Deep learning pneumoconiosis staging and diagnosis system based on multi-stage joint approach.BMC medical imaging · 2024Article
- DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.Scientific reports · 2024Article
- [A survey on the application of convolutional neural networks in the diagnosis of occupational pneumoconiosis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Artificial intelligence in advancing occupational health and safety: an encapsulation of developments.Journal of occupational health · 2024Review
- A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders.Cureus · 2024Review
- The application of metaverse in occupational health.Frontiers in public health · 2024Review
- Accurate pneumoconiosis staging via deep texture encoding and discriminative representation learning.Frontiers in medicine · 2024Article
- A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.Frontiers in public health · 2024Review
Corrections and comments
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Authors and funding
17 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to develop an artificial intelligence (AI)-based model to assist radiologists in pneumoconiosis screening and staging using chest radiographs. The model, based on chest radiographs, was developed using a training cohort and validated using an independent test cohort. Every image in the training and test datasets were labeled by experienced radiologists in a double-blinded fashion. The computational model started by segmenting the lung field into six subregions. Then, convolutional neural network classification model was used to predict the opacity level for each subregion respectively. Finally, the diagnosis for each subject (normal, stage I, II, or III pneumoconiosis) was determined by summarizing the subregion-based prediction results. For the independent test cohort, pneumoconiosis screening accuracy was 0.973, with both sensitivity and specificity greater than 0.97. The accuracy for pneumoconiosis staging was 0.927, better than that achieved by two groups of radiologists (0.87 and 0.84, respectively). This study develops a deep learning-based model for screening and staging of pneumoconiosis using man-annotated chest radiographs. The model outperformed two groups of radiologists in the accuracy of pneumoconiosis staging. This pioneer work demonstrates the feasibility and efficiency of AI-assisted radiography screening and diagnosis in occupational lung diseases.
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