## 4 Discussion  
Recently, deep learning has been widely applied in the medical field [27; 28; 29; 30]. In particular, deep learning demonstrates high performance in image classification, segmentation, and detection. Therefore, deep learning is used to diagnose diseases, such as specific diseases and abnormal signs in X-ray, CT, and MRI images [36; 37; 38; 39]. In addition, previous studies demonstrate that deep learning exhibits a reliable performance in the diagnosis of dental disease [40; 41; 42; 43]. Deep learning is a data-driven method of training. However, there are no studies and datasets for the detection of missing tooth regions for implant planning in panoramic radiographic images. Therefore, we constructed a dataset for a missing tooth region detection model and a tooth instance segmentation model. Several studies have applied deep learning to detect missing tooth regions and segment various anatomical structures for implant planning [8; 9]. [PERSON] et al. developed an AI system that detects canal, sinus, fossa, and missing teeth in CBCT images for implant planning [8]. [PERSON] et al. proposed a deep learning method for implant planning of the mandibular left first molar. Previous studies have established implant placement plans using CBCT images [8; 9]. However, CBCT requires a higher cost when compared to a panoramic radiographic X-ray. Additionally, previous studies have not fully automated methods and can only detect specific missing tooth regions. Therefore, there is a limit to detecting multiple missing tooth regions simultaneously [8; 9]. In clinical practice, implant placement is frequently performed on several teeth at the same time. Thus, we developed a detection model for identifying multiple missing tooth regions simultaneously for implant placement using panoramic radiographic images. Tooth instance segmentation was conducted to improve the performance of missing teeth region detection by only segmenting the teeth in the panoramic radiographic images. As a result, we achieved that the performance of tooth instance segmentation is 92.14% for mAP (0.5) and 76.78% for mAP (0.5:0.95), while the performance of missing tooth regions detection is 59.09% for mAP (0.5) and 20.40% for mAP (0.5:0.95). Since the oral structure and tooth size vary depending on the person, there was a limit to the detection performance of the missing tooth regions. A sufficient amount of data is required to achieve high-performance deep neural network, yet it is labor-intensive and expensive. Therefore, various previous studies have improved performance by using synthetic data when sufficient amounts of data are unavailable [44; 45; 46; 47; 48; 49]. Thus, we generated 37,323 synthetic datasets and used them to achieve high performance. However, the synthetic dataset was created from only 170 teeth masks. Thus, the synthetic dataset has limitations in diversity. Therefore, if more real data are accumulated, we expect the detection performance of the missing tooth regions to improve. For automatic dental implant placement planning, future work is required to develop a method that creates the position, axis, and size of the implant for the missing tooth regions \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **AP (0.5)** & **AP (0.5)** & **\#N** & **AP (0.5)** & **\#N** & **AP (0.5)** \\ \hline \#11 & 50.95\% & \#21 & 50.49\% & \#31 & - & \#41 & - \\ \#12 & - & \#22 & 50\% & \#32 & - & \#42 & - \\ \#13 & 100\% & \#23 & 66.99\% & \#33 & - & \#43 & - \\ \#14 & 46.73\% & \#24 & 63.99\% & \#34 & 100\% & \#44 & 0\% \\ \#15 & 67.82\% & \#25 & 70.26\% & \#35 & 73.88\% & \#45 & 82.26\% \\ \#16 & 38.5\% & \#26 & 74.32\% & \#36 & 91.11\% & \#46 & 89.74\% \\ \#17 & 32.53\% & \#27 & 26.51\% & \#37 & 27.50\% & \#47 & 37.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of missing teeth region detection model by tooth number.