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A Convolutional Neural Network for Automatic Tooth Numbering in Panoramic Images.

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  • معلومة اضافية
    • المصدر:
      Publisher: Wiley Country of Publication: United States NLM ID: 101600173 Publication Model: eCollection Cited Medium: Internet ISSN: 2314-6141 (Electronic) NLM ISO Abbreviation: Biomed Res Int Subsets: MEDLINE
    • بيانات النشر:
      Publication: 2024- : [Hoboken, NJ] : Wiley
      Original Publication: New York, NY : Hindawi Pub. Co.
    • الموضوع:
      Radiography, Panoramic/*methods ; Tooth/*diagnostic imaging; Adolescent ; Algorithms ; Data Collection/methods ; Humans ; Image Processing, Computer-Assisted/methods ; Neural Networks, Computer ; Oral Health
    • نبذة مختصرة :
      Competing Interests: The authors declare no conflict of interest.
      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.
      (Copyright © 2021 María Prados-Privado et al.)
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    • الموضوع:
      Date Created: 20211224 Date Completed: 20220119 Latest Revision: 20220119
    • الموضوع:
      20250114
    • الرقم المعرف:
      PMC8692013
    • الرقم المعرف:
      10.1155/2021/3625386
    • الرقم المعرف:
      34950732