## 1 Introduction  
Recently, deep learning has been applied in various fields with the goal of automation, and the field is rapidly growing [1, 2, 3]. In particular, deep learning is frequently applied in the medical field and shows high performance [4, 5, 6]. Deep learning is widely used to predict and diagnose diseases through image data such as MRI and CT images and signal data such as EEGs [7, 8, 9]. It can also be applied in the field of dentistry for the automatic diagnosis of various diseases [10, 11, 12, 13]. This has been applied to many studies, such as classifying cystic lesions in cone beam computed tomography (CBCT) images and estimating a person's age through their teeth [14, 15].  
Most people are born with mandibular third molars and have them removed for various reasons [16, 17]. Therefore, the extraction of mandibular third molars is a frequent operation in oral and maxillofacial surgery. According to the impaction type of the third molar, the presence of symptoms occurs at 30-68% after extraction of the third molar [18, 19]. Mandibular third molars grow in various positions and directions, so these have various impaction patterns [20, 21, 22, 23]. Therefore, it is important to determine the pattern of the mandibular third molar that is impacted before extraction to apply the appropriate surgical method according to the impaction pattern. There are several criteria for defining the impaction pattern of mandibular third molars. In certain impaction patterns, various problems may occur after extraction [24; 25; 26; 27]. Depending on the impaction pattern of the mandibular third molar, complications may arise after extraction. Several studies report that inferior alveolar nerve (IAN) injury is the most common complication after removing the mandibular third molar [28; 29; 30]. An IAN injury can cause several paresthesia in the mandible and due to the contradictory reported results on the efficacy of the proposed surgical therapeutics approaches, predicting potential complications is of paramount importance [31]. It can be predicted through the relationship between the mandibular third molar and the IAN, and there is a high likelihood of IAN injury when the mandibular third molar and the IAN make contact [32; 33].  
Several previous cases have applied deep learning models to conduct research related to the third molar. [PERSON] et al. proposed method to segment third molars and nerves using U-net [34]. [PERSON] et al. proposed method to predict the extraction difficulty of the mandibular third molar using convolution neural network(CNN) [35]. Utilizing panoramic radiographic images, we propose a model that uses a deep neural network to predict the extraction difficulty of mandibular third molars and the likelihood of IAN injury. First, we use a detection model to find mandibular third molars in panoramic radiographic images. Second, the area around the detected mandibular third molar is cropped to a specific size as a region of interest (ROI). Finally, we use the ROI as an input to the classification model to predict the extraction difficulty and likelihood of IAN injury (Figure 1).  
The automated prediction of extraction difficulty and IAN injury of mandibular third molars enables the reduction of the labor burden for dentists and shortens the diagnosis time. Patients can easily find out about their diagnosis, and recognize the risk of IAN injury  
Figure 1: Overall workflow.  
in advance of possible complications. The main contributions of this study are summarized as follows:  
* For the first time, we propose a method that can predict both the extraction difficulty of mandibular third molars and the likelihood of IAN injury following extraction.
* The proposed deep neural network ensures consistent performance because it uses the largest dental panoramic radiographic image dataset to our knowledge.
* We achieved high performance using classification models, with an extraction difficulty accuracy of 83.5%, AUROC of 92.79% and the likelihood of IAN injury accuracy of 81.1%, AUROC of 90.02%.