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Eichner classification based on panoramic X-ray images using deep learning: A pilot study.
Otsuka, Yuta; Indo, Hiroko; Kawashima, Yusuke; Tanaka, Tatsuro; Kono, Hiroshi; Kikuchi, Masafumi.
Afiliación
  • Otsuka Y; Department of Biomaterials Science, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan.
  • Indo H; Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan.
  • Kawashima Y; Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan.
  • Tanaka T; Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan.
  • Kono H; Department of Maxillofacial Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan.
  • Kikuchi M; Department of Biomaterials Science, Graduate School of Medical and Dental Sciences, Kagoshima University, Kagoshima, Japan.
Biomed Mater Eng ; 35(4): 377-386, 2024.
Article en En | MEDLINE | ID: mdl-38848165
ABSTRACT

BACKGROUND:

Research using panoramic X-ray images using deep learning has been progressing in recent years. There is a need to propose methods that can classify and predict from image information.

OBJECTIVE:

In this study, Eichner classification was performed on image processing based on panoramic X-ray images. The Eichner classification was based on the remaining teeth, with the aim of making partial dentures. This classification was based on the condition that the occlusal position was supported by the remaining teeth in the upper and lower jaws.

METHODS:

Classification models were constructed using two convolutional neural network

methods:

the sequential and VGG19 models. The accuracy was compared with the accuracy of Eichner classification using the sequential and VGG19 models.

RESULTS:

Both accuracies were greater than 81%, and they had sufficient functions for the Eichner classification.

CONCLUSION:

We were able to build a highly accurate prediction model using deep learning scratch sequential model and VGG19. This predictive model will become part of the basic considerations for future AI research in dentistry.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiografía Panorámica / Redes Neurales de la Computación / Aprendizaje Profundo Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: Biomed Mater Eng Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
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XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Radiografía Panorámica / Redes Neurales de la Computación / Aprendizaje Profundo Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: Biomed Mater Eng Asunto de la revista: BIOTECNOLOGIA / ENGENHARIA BIOMEDICA Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Países Bajos