The application of artificial intelligence to chest medical image analysis

Abstract

The aim of this article is to review recent progress in the application of artificial intelligence to chest medical image analysis. The lungs, bone, and mediastinum were included in terms of anatomy, while X-ray and computed tomography (CT), with and without contrast enhancement, were considered regarding imaging modalities. Four key components of deep learning were summarized, namely, network architectures, learning strategies, optimization methods, and vision tasks. Disease-specific applications were discussed in detail with respect to the dimension of the data input, network architecture, and modality: lung cancer, pneumonia, tuberculosis, pulmonary embolism, chronic obstructive pulmonary disease, and interstitial lung disease for lung; traumatic fractures, osteoporosis, osteoporotic fractures, and bone metastases for bone; and coronary artery calcification and aortic dissection for vascular diseases. Finally, five promising research directions and possible solutions were presented for future work.