## 3 Approaches
### Deep Neural Network Improves Fracture Detection by Clinicians  
This subsection focuses on the work of [PERSON] et al. [10].  
Aim: The aim of this work is to implement a tool that could help doctors in diagnosis, in order to distinguish if a wrist bone is fractured or not and which part of the bone is fractured.  
Dataset: The dataset was annotated by 18 senior sub-specialized orthopedic surgeons and consisted of 135,845 radiographs of a variety of body parts. Of these, 34,990 radiographs (training set) were posterior-anterior or lateral wrist views. The remaining 100,855 radiographs (pre-training set) belonged to 11 other body parts: foot, elbow, shoulder, knee, spine, femur, ankle, humerus, pelvis, hip, and tibia. Two datasets were used for clinical tests of the model. The first dataset (Test Set 1) consisted of 3500 wrist radiographs, randomly picked from the wrist dataset. The second dataset (Test Set 2) consisted of 1400 wrist radiographs, consecutively collected over a three-month period to ensure that the dataset was representative of a real-world clinical environment. Every training image was labelled with a bounding box drawn by a group of senior orthopedic surgeons who specialized in fractures. The training set has been split into 90% of the radiographs for training and 10% for validation. The data augmentation alterations included random rotations, cropping, horizontal mirroring, and lighting and contrast adjustments.  
Pre-processing: The images were pre-processed by rotating, cropping, and applying an aspect ratio, preserving rescaling operation to yield a fixed resolution of \(1024\times 512\).  
Methods: The model is a deep CNN, whose architecture is an extension of the common U-Net [11] model. The CNN has two outputs: the probability that the radiograph has a visible fracture and a heat map indicating for each location in the image the probability that the fracture is present in that location. The training of the model can be divided into two stages. In the first stage, the model was pre-trained on the pre-training set. In the second stage, the obtained model was fine-tuned using the training set, to specialize it to the task of detecting and localizing wrist fractures. The authors used a number of techniques to prevent overfitting, including early stopping, data augmentation and dropout with a probability of 0.5.  
Results: The model was tested on two different datasets: on Test Set 1, the model achieved an AUC of 0.967; on Test Set 2, the model achieved an AUC of 0.975. With the same set of images used for clinicians' evaluations, the model operated at 93.9% sensitivity and 94.5% specificity. After the training and testing phases of the CNN, the authors ran a controlled experiment with 40 emergency medicine clinicians, to evaluate each clinician's ability to detect fractures in wrist radiographs, both with and without the help of the system. Among these, 16 were physicians' assistants (PAs) and 24 were medical doctors (MDs). With the use of the proposed system, the MDs' average sensitivity and specificity improved by 10% and 7%, respectively, the PAs' average sensitivities and specificities improved by 12% and 6% respectively.  
Conclusion: This study showed that specialists' evaluations may be improved with the use of this system. This procedure should be applied in each work aimed at showing that a CAD system could help humans in evaluation. Pre-training the model before training it with the wrist bone images seem like a good procedure to adjust the parameters for the task, instead of using weights taken from a network that has been trained with a completely different dataset, e.g., ImageNet [12].