Prados-Privado María, García Villalón Javier, Blázquez Torres Antonio, Martínez-Martínez Carlos Hugo, Ivorra Carlos
Asisa Dental, Research Department, C/José Abascal, 32, 28003 Madrid, Spain.
Department of Signal Theory and Communications, Higher Polytechnic School, Universidad de Alcalá de Henares, Ctra. Madrid-Barcelona, Km. 33,600, 28805 Alcala de Henares, Spain.
J Clin Med. 2021 Mar 12;10(6):1186. doi: 10.3390/jcm10061186.
Dental radiography plays an important role in clinical diagnosis, treatment and making decisions. In recent years, efforts have been made on developing techniques to detect objects in images. The aim of this study was to detect the absence or presence of teeth using an effective convolutional neural network, which reduces calculation times and has success rates greater than 95%. A total of 8000 dental panoramic images were collected. Each image and each tooth was categorized, independently and manually, by two experts with more than three years of experience in general dentistry. The neural network used consists of two main layers: object detection and classification, which is the support of the previous one. A Matterport Mask RCNN was employed in the object detection. A ResNet (Atrous Convolution) was employed in the classification layer. The neural model achieved a total loss of 0.76% (accuracy of 99.24%). The architecture used in the present study returned an almost perfect accuracy in detecting teeth on images from different devices and different pathologies and ages.
口腔放射成像在临床诊断、治疗及决策过程中发挥着重要作用。近年来，人们致力于开发图像中物体检测技术。本研究的目的是使用一种有效的卷积神经网络来检测牙齿的有无，该网络可减少计算时间且成功率大于95%。共收集了8000张口腔全景图像。由两位具有三年以上普通牙科经验的专家对每张图像及每颗牙齿进行独立且手动的分类。所使用的神经网络由两个主要层组成：目标检测层和分类层，分类层以前者为支撑。目标检测采用了Matterport Mask RCNN。分类层采用了ResNet（空洞卷积）。该神经模型的总损失率为0.76%（准确率为99.24%）。本研究中使用的架构在检测来自不同设备、不同病理情况及不同年龄的图像中的牙齿时，返回了几乎完美的准确率。
