How to categorize a panoramic images database for automatically detection of dental treatments

  1. María Prados-Privado 12
  2. Javier García Villalón 2
  3. Rosa Rojo 23
  4. Antonio Blázquez Torres 2
  5. Carlos Hugo Martínez-Martínez 2
  6. Carlos Ivorra 2
  1. 1 Department of Signal Theory and Communications, Higher Polytechnic School, Universidad de Alcalá de Henares, Spain
  2. 2 Asisa Dental. Research Department, Madrid, Spain
  3. 3 Department of Dentistry, Faculty of Health Sciences, Alfonso X el Sabio University, Madrid, España
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Journal:
Academic Journal of Health Sciences: Medicina Balear

ISSN: 2255-0560

Year of publication: 2023

Volume: 38

Issue: 2

Pages: 73-77

Type: Article

DOI: 10.3306/AJHS.2023.38.02.73 DIALNET GOOGLE SCHOLAR lock_openIbdigital editor

Abstract

Objectives: The objective is to provide a methodology to obtain a categorized database without segmentation by sex or age that can be used in dental object detection applications and that may help in the diagnosis and usual clinical practice. Methods: A total of 10,677 panoramic images were analyzed by four examiners. In each tooth, the examiner indicated if the tooth exists or not and the position on FDI notation. After that, and for each tooth that exists, the examiner detailed whether or not there were the variables analyzed. Those variables were filled teeth, crown, implant, endodontic treatment, caries, and prosthetic. A descriptive study of inter-observer and intra-observer concordance-consistency was performed. Results: The results were statistically significant. Both teams obtained for all variables an almost perfect concordance k = 0.9 except in filled teeth where the kappa was k=0.8 and caries where a moderate agreement was obtained. The intra-examiner agreement was poor in caries variable and almost perfect in the rest of variables. Conclusions: A correctly categorized database is essential to obtain correct results in applications with artificial intelligence and neural networks. This study shows how to categorize a database of dental images for use in object detection applications in the field of dentistry.

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