# Automatic Tooth Detection and Numbering Using a Combination of a CNN and Heuristic Algorithm
## 5 Conclusions  
In this study, we presented an algorithm that can detect dental objects in a DPR image and assign a number to each tooth based on its shape and location as obtained using the RCNN and CNN algorithms. The results confirm that the numbering of the teeth and implants is possible in a DPR image. Based on the analysis results, an RCNN + heuristics algorithm, which exhibited the best performance in dental detection, was adopted. As a result, precision values of 84.5%, sensitivity values of 75.5%, and specificity values of 80.4% were achieved. Thus, the proposed algorithm was found to yield the best performance in detecting teeth.  
The panoramic images derived through the tooth numbering and implant fixture and crown analysis methods applied in this study share the following three purposes: to show and explain the panoramic image results to patients and non-professional personnel, statistical relevance, and an implementation in other algorithms. Although interpreting panoramic images is easy for dentists, patients face difficulties in such an interpretation. When a dentist needs to show and describe a panoramic tooth image to a patient, providing the tooth numbers on the crowns, implant fixtures, and teeth will help the patient easily understand the diagnostic information of the panoramic image. In addition, panoramic images can be used to identify a large number of teeth and dental objects and understand their statistical significance. Statistical information, including the number of patients with crowns and implant treatments, and specific tooth information based on the tooth number, can be used as an important tool in health-related dental research. Finally, panoramic images applying an automatic detection of the tooth number, as well as the position of the implant fixtures and crowns, can contribute to the development of radiology-related algorithms. Because existing methods generally provide tooth and implant information verbally, they have not been proven helpful in image training. Therefore, the proposed algorithm is expected to contribute to the development of image detection algorithms by providing additional tooth position information to existing methods.  
To develop the algorithm proposed in this study, we used a total of 253 teeth images and extracted 6446 teeth, implant, and crown data. Specifically, the detection processes for teeth, implant fixtures, and crowns were conducted first. Subsequently, we derived an algorithm that can number each tooth. The experimental results showed a high accuracy in tooth numbering, although the accuracy in terms of the numbering of the implant fixtures and crowns was lacking. The main reason for such accuracy is the smaller amount of training data for implant fixtures and crowns compared to that of teeth.  
Further, in the case of implant fixtures, the images of the training data had similar implant sizes and shapes, and hence the accuracy for detecting the implants was higher than that for the crowns.  
By contrast, the sizes and shapes of the crowns in the training data appeared to be different in each image, yielding a much lower detection rate during the training process. Accordingly, we expect to derive better results by training the model using a larger amount of teeth data in the future. In addition, to further increase the accuracy of the tooth numbering, the algorithm should be supplemented by improving the wisdom tooth detection rate, which we also expect to resolve by collecting additional data in the future.  
Conceptualization, C.K., D.K. and S.Y.; data, D.H., S.-J.Y., and H.J.; methodology, C.K., H.J. and S.-J.Y.; validation, C.K. and D.K.; formal analysis, C.K. and D.K.; investigation, C.K. and S.Y.; writing--original draft preparation, C.K.; writing--review and editing S.Y and H.J.; supervision, S.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Dongguk University Research Fund of 2016 and the Gerontechnology Research Center.  
The authors declare no conflict of interest.