## 4 Discussion  
Before third molar extraction, the clinician should plan a surgical method for the patient and consider potential complications such as IAN injury. Predicting extraction difficulty and the likelihood of IAN injury are essential in advance to plan a surgical method to minimize IAN injury, which is a complication that may occur after tooth extraction that requires gum incision, tooth segmentation, and bone fracture according to the extraction difficulty. As the population continues to increase, the number of patients who need third molar extraction continues to increase. As a result, dentist fatigue and burden from diagnosing and treating patients may increase, potentially decreasing diagnosis accuracy. Therefore, the demand for automatic diagnosis or prediction in oral and maxillofacial surgery as well as various other medical fields is increasing. Many studies have reported methods that can assist in clinical practice by reducing workload and fatigue, as well as providing guidance to inexperienced dentists. These methods were implemented using deep neural networks. Deep neural networks are applied to perform classification, detection, segmentation in various fields such as industry, medicine, and national defense. They show good performance. Therefore, we can also improve the efficiency and accuracy of diagnosis of third molar extraction using deep neural networks. Various studies in dentistry have applied deep neural networks. [PERSON] et al. estimated a person's age using CNN, the input of which was a manually cropped image of the first molar in the panoramic radiographic image [15]. [PERSON] et al. used CNN to classify third molar caries using a cropped image of the third molar in a panoramic radiographic image [49]. Moreover, there have been studies on third molar extraction using deep neural networks. [PERSON] et al. proposed a deep neural network that can detect mandibular third molars and IAN in panoramic radiographic images, noting that IAN injury can occur when mandibular third molars are extracted depending on the location relationship between mandibular third molar and IAN [34]. [PERSON] et al. detected mandibular third molars in panoramic radiographic images using SSD, an object detection model, and predicted extraction difficulty based on the Pederson difficulty score (PDS) as an input for the cropped image of detected mandibular third molar [35]. Additionally, they mentioned the importance of location and distance relationship between the IAN and mandibular third molar for the extraction difficulty of the mandibular third molar. Several previous studies have emphasized the significance of IAN injury [28; 29; 30]. Previous studies have limitations in that they do not predict the overall process of extraction difficulty and complications [35; 50]. Thus, we focused on the need for a technique that could predict the entire process from diagnosis to prognosis by predicting the extraction difficulty and possible complications. Medical data is generated by humans; therefore, it is difficult to generate data arbitrarily and limitations exist in class distribution and quantity. Our dataset has a class  
Figure 8: (**a**) The confusion matrix for the likelihood of IAN injury classification, (**b**) the ROC curve for the likelihood of IAN injury classification. imbalance that detrimentally affects the classification performance. Thus, we used the imbalanced dataset sampler to properly balance the classes in the dataset by increasing the proportion of minor classes and decreasing the proportion of major classes through resampling. Performance improvement was achieved by applying an imbalanced dataset sampler. We used Vision Transformer, one of the best models for classification performance, and confirmed the performance of Vision Transformer through comparative experiments with CNN. Our dataset contains many panoramic radiographic images created using various cone beam computed tomography (CBCT) techniques. Our deep neural networks were trained and validated using this dataset.