A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images.
Analysis of dental radiographs and images is an important and common part of the diagnostic process in daily clinical practice. During the diagnostic process, the dentist must interpret, among others, tooth numbering. This study is aimed at proposing a convolutional neural network (CNN) that perform...
| Publicado en: | BioMed Research International pp. 1 - 8 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/14/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154126195&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154126195 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/14/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 154126195 154126195 154126195 10.1155/2021/3625386 154126195 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images. aug: au: Prados-Privado, María García Villalón, Javier Blázquez Torres, Antonio Martínez-Martínez, Carlos Hugo Ivorra, Carlos affil: Asisa Dental, Research Department, C/José Abascal, 32, 28003 Madrid, Spain sug: subj: Tooth Radiography Radiography, Panoramic Image Processing, Computer Assisted Neural Networks (Computer) Evaluation Automation Human Dentition Nomenclature Deep Learning ab: Analysis of dental radiographs and images is an important and common part of the diagnostic process in daily clinical practice. During the diagnostic process, the dentist must interpret, among others, tooth numbering. This study is aimed at proposing a convolutional neural network (CNN) that performs this task automatically for panoramic radiographs. A total of 8,000 panoramic images were categorized by two experts with more than three years of experience in general dentistry. The neural network consists of two main layers: object detection and classification, which is the support of the previous one and a transfer learning to improve computing time and precision. A Matterport Mask RCNN was employed in the object detection. A ResNet101 was employed in the classification layer. The neural model achieved a total loss of 6.17% (accuracy of 93.83%). The architecture of the model achieved an accuracy of 99.24% in tooth detection and 93.83% in numbering teeth with different oral health conditions. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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