## 1 Introduction  
Dental implants are a common surgical procedure in oral and maxillofacial surgery [1]. Prior to implant placement surgery, it is essential to establish a surgical plan [2, 3, 4]. Generally, an implant placement plan is created using a patient's panoramic radiographic image or cone beam computed tomography (CBCT) image [5, 6, 7]. Implant placement is performed by finding missing tooth regions and determining a suitable implant product for the missing tooth regions [8, 9]. Therefore, missing tooth region detection precedes implant placement. Furthermore, automatic detection of missing tooth regions is essential for developing an automatic implant placement plan. A panoramic X-ray is one of the most commonly used diagnostic tools in modern dentistry along with CBCT due to its advantages of being more cost and time efficient than CBCT [10, 11, 12, 13, 14, 15, 16, 17]. Furthermore, only a panoramic radiographic image is used for diagnosis instead of CBCT because of the cost of the device and imaging of CBCT in some cases [18]. As a result, we used a panoramic radiographic image in this study to reduce the amount of computation. Due to the lack of automated technologies, clinicians had to plan dental implant placement manually. As a result, the diagnostic fatigue and burden on clinicians have steadily increased. Therefore, several studies have attempted to automatically detect missing tooth regions in order to generate an implant placement plan [8, 9]. One study uses deep learning to detect missing teeth in CBCT images for implant placement planning. Because the missing tooth regions are predicted using surrounding information such as the location, tilt, and placement of adjacent teeth, the prediction may be inaccurate when teeth are lost consecutively [8]. A deep neural network is used to detect the missing left first molar in panoramic radiographic images, and the position and axis of the implant are generated in 3D simulation [9]. However, the previous two studies using deep learning can only detect the missing region of the specific tooth and have limitations in detecting multiple missing tooth regions simultaneously [8; 9]. In actual clinical practice, multiple implants are frequently performed at the same time. Therefore, the detection of multiple missing tooth regions is necessary to establish multiple implant placement plans. Deep learning is a type of computing method that focuses on learning successive layers of increasingly meaningful representations from data [19]. Therefore, a deep learning model can extract meaningful representations from not only linear data but also complex data. As a result, deep learning is applied to data classification, segmentation, and detection in various domains, such as images and signals [20; 21; 22; 23; 24; 25; 26]. In particular, deep learning demonstrates high performance in medical imaging [27; 28; 29; 30]. Deep learning is also used to assist with various tasks, including caries detection and third molar extraction, in dentistry [14; 15; 31; 32]. In this work, we propose a deep learning method to detect missing tooth regions through panoramic images as a process for dental implant planning. This method consists of tooth instance segmentation, which segments all teeth except third molars by instance in panoramic radiographic images, and missing tooth regions detection, which identifies areas of multiple missing teeth. Previously, there were no datasets for both tooth instance segmentation and missing tooth region detection at the same time. Therefore, we built a dataset for tooth instance segmentation and missing tooth region detection. Furthermore, this study can be used for the production of dental implant surgical guides, diagnostic assistance for clinicians, and the education of unskilled apprentices. The goal of this work is to detect missing tooth regions as a process of automated dental implant placement. We propose a deep-learning-based method for the detection of missing tooth regions and tooth instance segmentation in panoramic radiographic images.