Artificial intelligence implementation in tooth identification from X-ray images

Abstract

Aim or Purpose: Development of a convolutional neural network that can precisely and quickly identify teeth from x-ray images, without using neighbouring structures as a frame of reference. Materials and Methods: Using a database of 11403 x-ray images that were precisely annotated by dental professionals we have trained, validated and tested a convolutional neural network (CNN) that can identify teeth according to their position in the oral cavity. Four “levels” were tested, the first one being classification according to the type of the tooth morphologically. This consisted of 4 categories: incisor, canine, premolar and molar. The second “level” added the differentiation between types of incisors, premolars and molars. This “level” had 8 categories, imitating a dental quadrant. The third “level” added maxillary or mandibular origin and a total of 16 categories. Finally, the fourth “level” had 32 categories, meaning every tooth had its own. Results: The first level offered an 97.83% accuracy on unseen data. The second level offered 92.13%. “Level” three offered 91.14%. The fourth level, while being the most demanding, offered a 91.13%. Conclusions: The results were the best in the 4 category “level” and the least successful in the 32 category “level”. Interestingly, the difference between the 32 and 16 category level was not significant at all. The developed CNN can identify the morphological type of the tooth with a very high accuracy rate. This opens a door into implementation of artificial intelligence in rapid analysis and cross referencing in (forensic) dental medicine.This study has been supported as a part of the Croatian Science Foundation under the project IP-2020-02-9423.