A Validation Employing Convolutional Neural Network for the Radiographic Detection of Absence or Presence of Teeth

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Prados Privado, María and García Villalón, Javier and Blázquez Torres, Antonio and Martínez Martínez, Carlos Hugo and Ivorra, Carlos (2021) A Validation Employing Convolutional Neural Network for the Radiographic Detection of Absence or Presence of Teeth. Journal of Clinical Medicine, 10 (6). p. 1186. ISSN 2077-0383

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Official URL: https://doi.org/10.3390/jcm10061186




Abstract

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.


Item Type:Article
Uncontrolled Keywords:teeth detection; neural network; panoramic images
Subjects:Medical sciences > Medicine > Dentistry
Medical sciences > Dentistry
ID Code:71304
Deposited On:22 Mar 2022 17:44
Last Modified:23 Mar 2022 10:22

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