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27.06.2022 | Original Article

Deep learning for preliminary profiling of panoramic images

verfasst von: Kiyomi Kohinata, Tomoya Kitano, Wataru Nishiyama, Mizuho Mori, Yukihiro Iida, Hiroshi Fujita, Akitoshi Katsumata

Erschienen in: Oral Radiology | Ausgabe 2/2023

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Abstract

Objective

This study explored the feasibility of using deep learning for profiling of panoramic radiographs.

Study design

Panoramic radiographs of 1000 patients were used. Patients were categorized using seven dental or physical characteristics: age, gender, mixed or permanent dentition, number of presenting teeth, impacted wisdom tooth status, implant status, and prosthetic treatment status. A Neural Network Console (Sony Network Communications Inc., Tokyo, Japan) deep learning system and the VGG-Net deep convolutional neural network were used for classification.

Results

Dentition and prosthetic treatment status exhibited classification accuracies of 93.5% and 90.5%, respectively. Tooth number and implant status both exhibited 89.5% classification accuracy; impacted wisdom tooth status exhibited 69.0% classification accuracy. Age and gender exhibited classification accuracies of 56.0% and 75.5%, respectively.

Conclusion

Our proposed preliminary profiling method may be useful for preliminary interpretation of panoramic images and preprocessing before the application of additional artificial intelligence techniques.

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Titel: Deep learning for preliminary profiling of panoramic images
verfasst von: Kiyomi Kohinata
Tomoya Kitano
Wataru Nishiyama
Mizuho Mori
Yukihiro Iida
Hiroshi Fujita
Akitoshi Katsumata
Publikationsdatum: 27.06.2022
Verlag: Springer Nature Singapore
Erschienen in: Oral Radiology / Ausgabe 2/2023
Print ISSN: 0911-6028
Elektronische ISSN: 1613-9674
DOI: https://doi.org/10.1007/s11282-022-00634-x

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Deep learning for preliminary profiling of panoramic images