## 1 Introduction  
In recent years, the number of requests for computed tomography (CT), magnetic resonance imaging (MRI), and, in general, radiology services has grown dramatically [1]. Nevertheless, there is a lack of radiologists due to recruitment challenges and many retirements. In this scenario, artificial intelligence (AI) can help radiologists in the time-consuming and challenging medical image analysis task. In any case, the AI-based tools do not replace medical staff, but assistive technologies prioritize, confirm, or validate radiologists' decisions and doubts. Deep learning, a branch of AI, has recently made substantial progress in analyzing images with a consequent better representation and interpretation of complex data. Inparticular, various works [2; 3; 4; 5; 6] deal with deep learning in orthopedic traumatology. However, the number of studies regarding deep learning on CT scans for fracture detection is low. Furthermore, building and training a neural architecture from scratch requires a huge amount of data. Image classification networks are trained on billions of data in the literature, using multiple servers running for several weeks [7]. This procedure is not feasible for most medical researchers. One way to overcome this obstacle is to use the so-called transfer learning. This process consists of adopting the highly refined characteristics of convolutional neural networks trained on millions of data and using them as a starting point for a new model. For example, to verify the extent of fracture detection on wrist radiographs, [PERSON] and [PERSON] [8] focus on transfer learning from a deep convolutional neural network (CNNs), pre-trained on non-medical images. Using the inception V3 CNN [9], they obtained an area under the receiver operating characteristic curve (AUC-ROC) of 0.95 on the test dataset. This result shows that a CNN pre-trained on non-medical images can be used for medical radiographs successfully. Another study was carried out by [PERSON] et al. [10], based on a CNN to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs. The deep neural network showed a similar performance to that of shoulder-specialized orthopedic surgeons, but better than that of the general physicians and the non-shoulder specialized orthopedic surgeons. This result denotes the possibility to diagnose fractures accurately by using plain radiographs automatically. Another study in this field was carried out by [PERSON] et al. [11], where they focused on detecting osteopororotic vertebral fractures on CT exams. Their system consisted of two blocks: (i) a CNN to extract radiological features from CTs; and (ii) a recurrent neural network (RNN) module to aggregate the previously extracted elements for the final diagnosis. The performance of the proposed system matched the ability of radiologist practitioners. Thus, the system could be used for screening and prioritizing potential fracture cases. Therefore, although several authors have already described some AI applications in the orthopedic field, the possibility to detect maxillofacial fractures in 3D images (CT scans) of injured patients using artificial neural networks, and in particular a transfer learning approach, has not been explored yet [12; 13; 14; 15]. This area's anatomical complexity and the specificity of this type of fracture make radiological diagnosis very often complex with a consistent risk of incongruuous hospitalizations. A fracture detection system based on AI able to detect the presence of maxillofacial fractures would be of great use in clinical practice by reducing the costs of treatment and discomfort for patients. This research aims to develop a fracture detection system, based on the transfer learning approach, able to predict the presence of maxillofacial fractures. The inputs for this system are the CT images of a patient after a trauma. The output of the system indicates the existence or not of a fracture.