Abstract: Deep learning-assisted diagnosis is an effective method to reduce missed and misdiagnosed fractures in the clinic. Currently, there are many research results on deep learning in fracture diagnosis, but there is a lack of review articles that summarizes and analyzes the current state of research in this field. Therefore, a summary of the existing literature in the field is presented in this paper. Firstly, fracture images and related datasets are introduced. Next, three deep learning-based fracture-assisted diagnosis methods are systematically described, and the deep learning models included in each method are compared. Then it classifies the methods according to different fracture types, and shows the deep learning methods in each type of fracture diagnosis. The analysis finds that the application and research of deep learning in the field of fracture diagnosis has made significant progress, and the model performance can be comparable to that of clinicians. However, models are highly influenced by the data set during training, and new models and techniques are more difficult to implement. Deep learning-assisted fracture diagnosis still has more room for development.

Key words: deep learning, image dataset, fracture diagnosis

摘要： 深度学习辅助诊断是减少临床中骨折漏诊误诊的有效方法。目前，深度学习在骨折诊断中的研究成果较多，但缺少对该领域研究现状进行总结分析的综述性文章。对领域内现有的文献进行总结；介绍骨折影像及相关数据集；系统地阐述三种基于深度学习的骨折辅助诊断方法，对各方法中包含的深度学习模型进行比较；按照不同骨折类型进行分类，对各类型骨折诊断中深度学习方法的应用进行展示。分析发现，深度学习在骨折诊断领域的应用和研究已取得显著进展，模型性能可与临床医生相当。但模型在训练时受数据集的影响较大，新的模型和技术较难得到实施。深度学习辅助骨折诊断仍有较大的发展空间。

关键词: 深度学习, 影像数据集, 骨折诊断