## 3 Approaches
### Artificial Intelligence for Analysing Orthopaedic Trauma Radiographs
Conclusion: This is the only work we found that implements the technology of R-CNN not only to classify fractures, but also to detect the exact region of the fracture with a high accuracy (the results were demonstrated to be significantly more accurate than the detection achieved by physicians and radiologists). The authors, smartly, did not use shear, strain or spot noise augmentation since these transformations could cause a normal hand image to be classified as a hand with a fracture. The authors tested the network with different resolution images as inputs in order to understand what the image size should be to feed into the network; this proved to be \(1300\times 1300\) pixels. The images have been labelled by just one specialist; however, it would have been better to have a second opinion. Application of a Deep Learning Algorithm for Detection and Visualization of Hip Fractures on Plain Pelvic Radiographs  
This subsection focuses on the work of [PERSON] et al. [21]. Aim: The aim of this work is to use a CNN (pre-trained with a dataset of medical images) to classify and localize hip fractures in plain frontal pelvic radiographs (PXRs). The localization phase is implemented by the use of gradient-weighted class activation mapping (Grad-CAM) [22] to confirm the validity of the model. Dataset: We can distinguish three different datasets: the first is the pre-training dataset (named A), composed of 25,505 limb radiographs (6019 ankles, 3832 elbows, 4134 feet, and 3378 wrists). The second is the plain frontal pelvic dataset (named B), composed of 3605 images. The last one (named C) is composed of 100 independent PRXs (25 femoral neck fractures, 25 intertrochanteric fractures, 50 without hip fractures) acquired subsequently for a second test. Images were initially labelled as fracture or no-fracture according to the previous diagnosis, then a group of radiologists reviewed every images to confirm the labelling. During the training process, image augmentation was applied with a zoom of 10%, horizontal flip, vertical flip, and rotation of 10\({}^{\circ}\). Pre-processing: Poor-quality images were excluded. The input images were resized to \(512\times 512\) pixels to reduce the complexity and computation. Methods: The authors used the DenseNet [23] network for the classification task. The majority of the papers from this review used ImageNet to pre-train the network; instead, in this work, the authors decided to pre-train the network with the A dataset. Hence, the network was initially trained to identify the body part in each limb radiograph. The pre-trained network was then fed with the B dataset to recognize if the bone was broken or not. Finally, to demonstrate that the CNN was actually focusing on the right area of the images, the authors implemented Grad-CAM to generate a heat map in the images that the network classified as fractured. Results: During the pre-training phase, the model showed an accuracy of 99.5% for the test dataset in recognizing specific body parts. When the task was changed to detect hip fracture, the final test accuracy was 90% and the AUC was 0.98. After applying the hip model to the C dataset, the accuracy, sensitivity and specificity of the model were 91%, 98% and 84%, respectively. A web-based questionnaire was made to compare these results with a total of 21 specialists. The range of sensitivity of primary physicians (except radiologists and orthopedic surgeons) was 84% to 100%, and the specificity ranged from 46% to 94%. The experts, including two radiologists and four orthopedic surgeons, completed the questionnaire and achieved a mean sensitivity of 99.3% and a specificity of 87.7%. Furthermore, the heat maps computed with Grad-CAM were reviewed: after analyzing 49 heat map images, only two images identified the wrong activation site. Conclusion: Pre-training the network with limb images instead of ImageNet increased the accuracy from 79% to 91%. This shows that is better to pre-train a network with more related images.