## 2 AI for the Study of Orbital Fractures
### Applications of AI in Orbital Fractures  
Several areas of AI application in orbital fractures are reported in the literature. Regarding the diagnosis of orbital fractures, there are several deep learning (DL) models, including DenseNet, InceptionV3, and YOLOv8, which have been trained on CT images to identify the presence and location of the fracture. The study conducted by [PERSON] et al. (2025) [14] proposed an algorithm that could distinguish whether the fracture is acute or chronic and whether it localizes to the floor, medial wall, or both, achieving 99.5% accuracy. The study conducted by [PERSON] et al. (2024) [15] also tested object detection systems (YOLOv8 and SSD) to rapidly identify multiple facial fractures, including orbital fractures, in emergency room settings. Another application is anatomical segmentation of the orbit, which is essential for volume calculation, preoperative simulation, and 3D model printing. [PERSON] et al. (2023) [16] used a combination of two DL models (DenseNet-69 and U-Net) for automatic orbit segmentation and the identification of fractured regions, obtaining a superior Dice score of 0.88 and an AUC of 0.99. In addition, the study conducted by [PERSON] et al. (2023) [17] compared the results obtained from U-Net2D with those obtained from the study of 3D-printed models, obtaining compatible performance. In addition, the study conducted by [PERSON] et al. (2020) [18] showed that the use of MRes-UNET2D enables precise segmentation of the eyeball for volumetric calculation, which is an important finding in cases of trauma. Another interesting and innovative application is the automatic reconstruction of the orbital wall. Here, AI models are used to predict the original shape of the orbit, which is useful in cases of bilateral fractures or complex defects in which it is difficult to determine what the original anatomical shape of the orbit might be. [PERSON] et al. (2025) [19] developed a 3D U-Net-based system capable of generating virtual reconstructions of the injured orbital wall, obtaining excellent results in terms of Hausdorff Distance at the 95 th percentile (HD95) of <2.5 mm and a Structural Similarity Index (SSIM) greater than 0.98. In addition, Generative Adversarial Networks (GANs) have their place in this domain. In fact, the study conducted by [PERSON] et al. (2025) [20] used a GAN network to generate highly symmetric and personalized surgical schedules with DSC values of >92% and Hausdorff Distance of less than 0.6 mm. Finally, a widely used and widely accessible tool was tested as a clinical decision support, namely ChatGPT-4. The study conducted by [PERSON] et al. (2024) [21] evaluated the performance of ChatGPT-4 in deciding whether an orbital fracture required surgical or conservative treatment. The model was able to correctly diagnose all fracture cases and indicate which patients required surgical treatment, while still having limitations in identifying those who would benefit from conservative treatment. The following table (Table 2) shows the studies found in the literature regarding the application of artificial intelligence models in the diagnosis and study of orbital fractures. Information such as the models used, the input data processed by the algorithms, the evaluation method, the results, and the main benefits obtained from the individual studies are also given.