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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Abbreviations
- AI:
-
Artificial intelligence
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Acknowledgements
We thank Ryan Chastain-Gross, Ph.D., from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.
Funding
This work was supported by JSPS KAKENHI Grant Number JP19K10347.
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Asahi University School of Dentistry ethics committee (approval no. 31040).
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Kohinata, K., Kitano, T., Nishiyama, W. et al. Deep learning for preliminary profiling of panoramic images. Oral Radiol 39, 275–281 (2023). https://doi.org/10.1007/s11282-022-00634-x
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DOI: https://doi.org/10.1007/s11282-022-00634-x