Department of Environmental Medicine, Kochi Medical School, Nankoku, Kochi, Japan.
School of Information, Kochi University of Technology, Nankoku, Kochi, Japan.
Semin Respir Crit Care Med. 2023 Jun;44(3):362-369. doi: 10.1055/s-0043-1767760. Epub 2023 Apr 18.
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.
职业性肺部疾病表现出复杂的影像学表现，长期以来一直是计算机辅助诊断（CAD）的挑战。这一历程始于 20 世纪 70 年代，当时纹理分析被开发并应用于弥漫性肺部疾病。尘肺在 X 光片上表现为小结节、大结节和胸膜阴影的组合。国际劳工组织尘肺放射分类是描述尘肺的主要工具，也是一个理想的系统，可以通过人工智能（AI）进行 CAD 适配。人工智能包括机器学习，它利用深度学习或人工神经网络。这反过来又包括卷积神经网络。CAD 的任务被系统地描述为目标病变的分类、检测和分割。Alex-net、VGG16 和 U-Net 是用于开发弥漫性肺部疾病（包括职业性肺部疾病）诊断系统的最常用算法之一。我们描述了追求尘肺 CAD 的漫长历程，包括我们最近提出的一个新的专家系统。
