Artificial Intelligence in Quantitative Chest Imaging Analysis for Occupational Lung Disease

Occupational lung disease manifests complex radiologic findings which have long been a challenge for computer-assisted diagnosis (CAD). This journey started in the 1970s when texture analysis was developed and applied to diffuse lung disease. Pneumoconiosis appears on radiography as a combination of small opacities, large opacities, and pleural shadows. The International Labor Organization International Classification of Radiograph of Pneumoconioses has been the main tool used to describe pneumoconioses and is an ideal system that can be adapted for CAD using artificial intelligence (AI). AI includes machine learning which utilizes deep learning or an artificial neural network. This in turn includes a convolutional neural network. The tasks of CAD are systematically described as classification, detection, and segmentation of the target lesions. Alex-net, VGG16, and U-Net are among the most common algorithms used in the development of systems for the diagnosis of diffuse lung disease, including occupational lung disease. We describe the long journey in the pursuit of CAD of pneumoconioses including our recent proposal of a new expert system.

Automated summary

Occupational lung disease presents complex radiologic findings, making it a challenge for computer-assisted diagnosis (CAD). Texture analysis was developed in the 1970s for diffuse lung disease. Pneumoconiosis appears on X-rays as a combination of small opacities, large opacities, and pleural shadows. The International Classification of Radiograph of Pneumoconioses is an ideal system for CAD using artificial intelligence (AI), which includes machine learning and convolutional neural networks. Common algorithms such as Alex-net, VGG16, and U-Net are used for the diagnosis of diffuse lung disease, including occupational lung disease. We describe the long journey in the development of CAD for pneumoconioses and propose a new expert system.