## 3 Approaches
### Artificial Intelligence for Analysing Orthopaedic Trauma Radiographs  
This subsection focuses on the work of [PERSON] et al. [15]. Aim: The aim of this study was to assess if standard deep learning networks can be trained to identify if a bone is fractured or not in orthopedic radiographs. Secondarily, the authors also examined whether deep learning could be used to determine additional features such as body part, exam view, and laterality. We focused the discussion only on the fracture classification in this review. Dataset: The dataset was composed of 256,458 hand, wrist and ankle radiographs, with associated radiologist reports. Labels were extracted from digital imaging and communications in medicine (DICOM) headers. In total, 56% of the images contained fractures. The data was split into 70% training images, 20% validation images and 10% test images. Pre-processing: Each image was cropped and rescaled to \(256\times 256\) pixels, about 10-20% of original size. As the authors tested different network architecture, an additional rescaling was performed to match the predefined image size of each network. Methods: The authors selected five common deep networks for this task: a BVLC Reference CaffeNet network (eight layers), a VGG CNN S network (eight layers), a VGG CNN (16 and 19 layer networks) and a network-in-network (14 layers). The networks were pre-trained on the ImageNet dataset and the last fully connected layer was replaced in order adapt the network for this specific task. The learning rate was adapted at the end of each epoch. Results: Comparing the result of the five different networks, the best raw performance was exhibited by the VGG16 [16] network, with an accuracy of 83%. To assess a network's performance and understand where it fails, the authors tested 400 images from the test dataset and compared the network with human performance by allowing two senior orthopedic consultants to identify fractures in the same 400 images at the same resolution as the network. As VGG16 had the best performance in the fracture class, the authors selected it for manual review. When comparing the network with the two senior orthopedic surgeons, they found that the network performed in a similar manner to the humans. Conclusion: Testing different existing networks and choosing the one that performs best is a good practice in the field of neural networks. This approach might be useful for classifying fractures at different skeletal sites. The dataset contained a really high number of images, the highest among the datasets used in the papers we reviewed. This is obviously one of the most fundamental aspects when working with deep learning. With a huge dataset, is not easy to label the images manually, and that is why the authors decided to label them automatically from the hospital information. This procedure is subject to errors and a second review may be useful. Artificial Intelligence in Fracture Detection: Transfer Learning from Deep Convolutional Neural Networks  
This subsection focuses on the work of [PERSON] and [PERSON] [17]. Aim: The aim of this paper is to use transfer learning in order to classify wrist fractures in two classes: broken and unbroken. According to the authors, this was the first work where transfer learning from pre-trained CNNs has been successfully applied to the problem of fracture detection in plain radiographs. Dataset: The final dataset was composed of 1389 images, 695 wrist radiographs showing a fracture and 694 showing no fracture. This classification was obtained from the radiological report and subsequently checked and verified by a radiology registrar with three years' radiology experience. Subsequently, data augmentation was applied: horizontal flip, rotation (between 0\({}^{\circ}\) and 25\({}^{\circ}\)), width and height shift (by a factor of 0%-15%), shearing (between 0%-10%) and zoom (between 0%-15%). This resulted in an overall amplification by a factor of eight, with 5560 images in the fracture group and 5552 images in the no-fracture group.