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| 深度学习在椎体骨质疏松及其骨折的研究进展 |
| Research progress of deep learning in the evaluation of vertebral osteoporosis and related fractures |
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| DOI：10.3969/j.issn.1006-7108.2024.02.022 |
| 中文关键词: 骨质疏松 骨质疏松性椎体压缩骨折 深度学习 电子计算机断层扫描 磁共振成像 |
| 英文关键词:osteoporosis osteoporotic vertebral compression fracture deep learning computed tomography magnetic resonance imaging |
| 基金项目:南方医科大学第三附属医院院长基金项目（YM2021012） |
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| 中文摘要: |
| 随着人口老龄化的发展，骨质疏松症（osteoporosis，OP）的发病率逐年上升，骨质疏松性椎体压缩骨折（osteoporotic vertebral compression fractures，OVCF）是其最常见的严重并发症，电子计算机断层扫描（computed tomography，CT)与磁共振成像（magnetic resonance imaging，MRI）技术相较于双能X线吸收法对椎体OP及OVCF的评估具有独特优势。深度学习技术作为医学图像分析的新方法，可对CT及MRI图像中的椎体进行自动分割、分类及检测。笔者主要对CT和MRI分别结合深度学习在评估椎体OP及相关骨折方面的研究进行综述。 |
| 英文摘要: |
| With the development of the aging population, the incidence of osteoporosis increases year by year. Osteoporotic vertebral compression fracture is the most common serious complication. CT and MRI have unique advantages compared with dual-energy X-ray absorption in the evaluation of osteoporosis and osteoporotic vertebral compression fracture. As a new method of medical image analysis, deep learning technology can automatically segment, classify, and detect vertebrae in CT and MRI images. This article mainly reviews the studies on the evaluation of vertebral osteoporosis and related fractures by CT and MRI combined with deep learning respectively. |
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