Review of Applications of Deep Learning in Fracture Diagnosis

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. © 24 Editorial Department of Science and Technology of Food Science. All rights reserved.