医学
尘肺病
计算机辅助设计
胸片
职业性肺病
射线照相术
肺
卷积神经网络
放射科
人工智能
间质性肺病
病理
内科学
计算机科学
工程制图
工程类
作者
Narufumi Suganuma,Shigeaki Yoshida,Yuma Takeuchi,Yoshua K. Nomura,Kazutoshi Suzuki
出处
期刊:Seminars in Respiratory and Critical Care Medicine
[Georg Thieme Verlag KG]
日期:2023-04-18
卷期号:44 (03): 362-369
被引量:1
标识
DOI:10.1055/s-0043-1767760
摘要
Abstract 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.
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