# Automatic Tooth Detection and Numbering Using a Combination of a CNN and Heuristic Algorithm
## 2 Prior Research
### Dental Radiology  
Various studies on tooth detection and numbering have been conducted. Early tooth numbering studies were primarily performed on periapical images. Studies conducted in 2005 [18; 19] used pattern recognition classification to classify teeth as molars and premolars and assigned numbers to each tooth according to its position.  
Studies on tooth detection using CNNs began in 2017. Automated tooth detection and numbering were conducted using a CNN, and a heuristic method was applied to tooth detection [12]. To detect an object as a tooth and process the tooth number, a heuristic method was used to detect the number corresponding to each tooth [20].  
In 2018, the segmentation of each tooth was carried out using a DPR image. ResNet101, which is the basis of a Mask R-CNN, was used in these tooth studies. With this approach, the categories are divided into ten different classes. During learning, only images of teeth without an implant are trained, and the learning result for tooth detection was found to achieve an accuracy of 95% [9].  
In 2019, tooth X-ray images were evaluated using a VGG16 CNN model for tooth detection and numbering. However, this study has limited applications i.e., periapical images and DPR images without implants. Therefore, it was found to have limited use in the real world [10].  
In 2020, Researchers in Japan conducted research on implant fixtures and developed an algorithm to classify implants by implant company and model. This algorithm judges which implant fixture is the implant fixture when there is a defined ROI area, and there is a lack of locating the implant [21].  
The Table 1 shows that recent studies on the tooth detection, segmentation and numbering algorithm through CNN. The high accuracy of tooth detection and numbering has achieved in the recent studies, implant fixtures and crowns didn't included. Our algorithm is proposed for modeling, detecting, and numbering implant fixtures, crowns, and normal teeth using complex panoramic images that include information on various dental treatments.  
In our study, an algorithm is proposed to detect teeth, implant fixtures, and crowns, and subsequently number the teeth and implants in a DPR image based on the FDI two-digit notation [22]. This algorithm can be used universally, including cases of DPR images of implanted teeth. The notation consists of two digits, where the first digit indicates one of the four tooth quadrants, and the second digit indicates the detailed shape of the tooth.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Architecture** & **Object** & **Evaluation** & **Pros (+)/Cons (\(-\))** & **Ref.** & **Year** \\ \hline \multirow{4}{*}{Oktay} & \multirow{4}{*}{CNN} & Incisors, & & (+) CNN Approach & & \\  & & Premolars, & Over 90\% & (\(-\))Few Dataset & 17 & 2017 \\  & & Molars & & (\(-\)) Manual ROI Selection & & \\ \multirow{2}{*}{[PERSON] et al.} & \multirow{2}{*}{Mask R-CNN} & \multirow{2}{*}{Tooth} & \multirow{2}{*}{94\%} & (+) Region Segmentation & & \\  & & & & (\(-\)) Without Implants & & \\ \multirow{2}{*}{[PERSON] et al.} & \multirow{2}{*}{R-CNN} & \multirow{2}{*}{Tooth} & \multirow{2}{*}{99\%} & (+) R-CNN Approach & & \\  & & & & (+) Many Data & 19 & 2019 \\ \multirow{4}{*}{[PERSON] et al.} & \multirow{4}{*}{CNN} & \multirow{2}{*}{Implants} & \multirow{2}{*}{Over 90\%} & (+) CNN Approach & & \\  & & & & (+) Implant & 26 & 2020 \\ \cline{1-1}  & & & & (\(-\)) Only Implants & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative overview table for machine learning article in the field of tooth detection.