Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays.
Authors
Affiliations (5)
Affiliations (5)
- Bioengineering, Automation and Robotics Research Group, Department of Automation Engineering, Electronics and Computer Architecture and Networks, School of Engineering, University of Cadiz, Puerto Real, 11519, Cádiz, Spain; Biomedical Research and Innovation Institute of Cadiz (INiBICA), Puerta del Mar University Hospital, Cádiz, 11009, Spain. Electronic address: [email protected].
- Bioengineering, Automation and Robotics Research Group, Department of Automation Engineering, Electronics and Computer Architecture and Networks, School of Engineering, University of Cadiz, Puerto Real, 11519, Cádiz, Spain; Biomedical Research and Innovation Institute of Cadiz (INiBICA), Puerta del Mar University Hospital, Cádiz, 11009, Spain.
- Radiology Department, Puerto Real University Hospital, Puerto Real, 11510, Cádiz, Spain.
- Radiology Department, Puerta del Mar University Hospital, Cádiz, 11009, Spain.
- Biomedical Research and Innovation Institute of Cadiz (INiBICA), Puerta del Mar University Hospital, Cádiz, 11009, Spain; Pulmonology Department, Puerta del Mar University Hospital, Cádiz, 11009, Spain.
Abstract
Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be a significant global health issue, especially with the increasing use of engineered stone (ES) surfaces containing high silica content. Traditional diagnostic methods, dependent on radiological interpretation, have low sensitivity, especially, in the early stages of the disease, and present variability between evaluators. This study explores the efficacy of deep learning techniques in automating the screening and staging of silicosis using chest X-ray images. Utilizing a comprehensive dataset, obtained from the medical records of a cohort of workers exposed to artificial quartz conglomerates, we implemented a preprocessing stage for rib-cage segmentation, followed by classification using state-of-the-art deep learning models. The segmentation model exhibited high precision, ensuring accurate identification of thoracic structures. In the screening phase, our models achieved near-perfect accuracy, with ROC AUC values reaching 1.0, effectively distinguishing between healthy individuals and those with silicosis. The models demonstrated remarkable precision in the staging of the disease. Nevertheless, differentiating between simple silicosis and progressive massive fibrosis, the evolved and complicated form of the disease, presented certain difficulties, especially during the transitional period, when assessment can be significantly subjective. Notwithstanding these difficulties, the models achieved an accuracy of around 81% and ROC AUC scores nearing 0.93. This study highlights the potential of deep learning to generate clinical decision support tools to increase the accuracy and effectiveness in the diagnosis and staging of silicosis, whose early detection would allow the patient to be moved away from all sources of occupational exposure, therefore constituting a substantial advancement in occupational health diagnostics.