Abstract: Osteoporosis, a systemic disease with an increased risk of fracture, is caused by a decrease in bone density. In clinical practice, the diagnosis is based on the imaging examination. In recent years, deep learning methods have made breakthroughs in the field of skeletal medical image processing. This paper compares and summarizes the deep learning methods that have been used in osteoporosis assisted diagnosis. Firstly, it introduces the commonly used datasets. Secondly, it systematically expounds the application of convolutional neural network, recurrent neural network, deep belief network and generative adversarial network in the classification of osteoporosis. And then, the application of full convolutional networks and U-Net in the segmentation of the lesion area of osteoporosis is described. And it introduces the model of the latest AI ChatGPT potential applications. Then it compares the performance of the different models. Finally, the paper points out the existing difficulties and puts forward the corresponding prospects in this field.

Key words: osteoporosis, deep learning, computer aided diagnosis, convolutional neural network

摘要： 骨质疏松症是一种由于骨密度下降引起骨折危险性增加的全身性疾病，临床上以影像学检查作为诊断依据。近几年深度学习方法在骨骼医学图像处理领域取得突破性进展，针对骨质疏松辅助诊断中所采用的深度学习方法进行了梳理总结。介绍了常用的影像学数据集，系统阐述了卷积神经网络、循环神经网络、深度置信网络、生成对抗网络在骨质疏松分类中的应用，阐述了全卷积网络、U-Net在骨质疏松病灶区域分割中的应用，同时介绍了最新AI模型ChatGPT的潜在应用，比较不同模型的性能，指出该领域目前存在的难点并提出相应的展望。

关键词: 骨质疏松症, 深度学习, 计算机辅助诊断, 卷积神经网络