Atypical Femur Fracture(AFF) is a type of stress fracture that occurs in conjunction with prolonged bisphosphonate treatment. In practice, AFF is very rarely identified from Normal Femur Fracture(NFF) correctly on the first diagnostic X-ray examination. This project aims at developing an algorithm based on deep neural networks to assist clinicians with the diagnosis of atypical femurfracture. Two diagnostic pipelines were constructed using the Convolutional Neural Network (CNN) as the core classifier. One is a fully automatic pipeline, where the X-rays image is directly input into the network with only standardized pre-processing steps. Another interactive pipeline requires the user to re-orient the femur bones above the fractures to a vertical position and move the fracture line to the image center, before the repositioned image is sent to the CNNs. Three most popular CNNs architectures, namely VGG19, InceptionV3 and ResNet50,were tested for classifying the images to either AFF or NFF. Transfer learning technique was used to pre-train these networks using images form ImageNet. The diagnosis accuracy was evaluated using 5-fold cross-validation. With the fully automatic diagnosis pipeline, we achieved diagnosis accuracy of 82.7%, 89.4%, 90.5%, with VGG19, InceptionV3 and ResNet50, respectively. With the interactive diagnostic pipeline, the diagnosis accuracy was improved to 92.2%, 93.4% and 94.4%, respectively. To further validate the results, class activation mapping is used for indicating the discriminative image regions that the neural networks learn to identify a certain class.

The rarity and subtle radiographic features of atypical femoral fractures (AFF) make it difficult to distinguish radiologically from normal femoral fractures (NFF). Compared with NFF, AFF has subtle radiological features and is associated with the long-term use of bisphosphonates for the treatment of osteoporosis. Automatically classifying AFF and NFF not only helps improve the diagnosis rate of AFF but also helps patients receive timely treatment. In recent years, automatic classification technologies for AFF and NFF have continued to emerge, including but not limited to the use of convolutional neural networks (CNNs), vision transformers (ViTs), and multimodal deep learning prediction models. The above methods are all based on deep learning and require the use of centralized radiograph datasets. However, centralizing medical radiograph data involves issues such as patient privacy and data heterogeneity. Firstly, radiograph data is difficult to share among hospitals, and relevant laws or guidelines prohibit the dissemination of these data; Second, there were overall radiological differences among the different hospital datasets, and deep learning does not fully consider the fusion problem of these multi-source heterogeneous datasets. Based on federated learning, we implemented a distributed deep learning strategy to avoid the use of centralized datasets, thereby protecting the local radiograph datasets of medical institutions and patient privacy. To achieve this goal, we studied approximately 4000 images from 72 hospitals in Sweden, containing 206 AFF patients and 744 NFF patients. By dispersing the radiograph datasets of different hospitals across 3-5 nodes, we can simulate the real-world data distribution scenarios, train the local models of the nodes separately, and aggregate the global model, combined with percentile privacy protection, to further protect the security of the local datasets; in addition, we compare the performance of federated learning models using different aggregation algorithms (FedAvg, FedProx, and FedOpt). In the end, the federated learning global model we obtained is better than these local training models, and the performance of federated learning models is close to the performance of the centralized learning model. It is even better than the centralized learning model in some metrics. We conducted 3-node and 5-node federation learning training respectively. Limited by the data set size of each node, 5-node federated learning does not show any more significant performance than 3-node federated learning. Federated learning is more conducive to collaborative training of high-quality prediction models among medical institutions, but also fully protects sensitive medical data. We believe that it will become a paradigm for collaborative training models in the foreseeable future.