AI-based determination of Kennedy classification and modification spaces on panoramic radiographs

Aliye İpek Kuşçu; Fatma Nur Ortataş; Ayça Dolar; Süha Kuşçu

doi:10.32322/jhsm.1870007

AI-based determination of Kennedy classification and modification spaces on panoramic radiographs

Abstract

Aims: The aim of this study is to automatically perform tooth segmentation, FDI (Fédération Dentaire Internationale) tooth numbering, and the Artificial Intelligence (AI)-based determination of Kennedy classification and its modifications using images obtained from panoramic dental radiographs. The study aims to help clinical decision support processes in prosthetic dentistry. Methods: The U-Net architecture was used for pixel-level tooth segmentation on panoramic dental radiographs, and different pre-trained encoder backbones were compared. Segmentation performance was evaluated using the Dice Similarity Coefficient and Intersection over Union metrics. The outputs of the best-performing model were integrated with the FDI tooth numbering system to automatically determine Kennedy classification and modification areas. Results: The results were evaluated by two clinicians specialized in prosthetic dentistry. Findings the U-Net model with the ResNet34 encoder demonstrated higher and more balanced segmentation performance compared to the other architectures. Statistical analyses revealed that the ResNet34 model was significantly superior to the other encoder configurations used in the study (p<0.05). The FDI-based analysis of the segmentation outputs showed that Kennedy classification and its modifications could be automatically determined in accordance with clinical rules. Conclusion: This study demonstrates that Kennedy classification and its modifications can be automatically determined using AI-assisted analysis of panoramic dental radiographs and provides a robust framework for decision support systems in prosthetic dentistry. Unlike prior AI-based studies that address Kennedy classification only at the basic class level, the present study explicitly and systematically incorporates modification space determination, representing a more detailed and systematic approach toward automated prosthetic assessment.

Keywords

Supporting Institution

There is a no Supporting Institution.

Project Number

There is a no Project Number.

Ethical Statement

There is a no Ethical Statement.

Thanks

The authors would like to sincerely thank the entire research team for their valuable contributions, collaborative efforts, and continuous support throughout the study. Their dedication and expertise played a crucial role in the successful completion of this work.

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Details

Primary Language

English

Subjects

Prosthodontics

Journal Section

Research Article

Authors

Aliye İpek Kuşçu ^*
0000-0002-6696-1256
Türkiye

Fatma Nur Ortataş
0000-0001-7897-9958
Türkiye

Ayça Dolar
0009-0005-5556-2718
Türkiye

Süha Kuşçu
0000-0002-0805-5888
Türkiye

Publication Date

May 19, 2026

Submission Date

January 23, 2026

Acceptance Date

April 7, 2026

Published in Issue

Year 2026 Volume: 9 Number: 3

DOI

https://doi.org/10.32322/jhsm.1870007

IZ

https://izlik.org/JA37NE96BP

Cite

RIS / Bibtex

APA

Kuşçu, A. İ., Ortataş, F. N., Dolar, A., & Kuşçu, S. (2026). AI-based determination of Kennedy classification and modification spaces on panoramic radiographs. Journal of Health Sciences and Medicine, 9(3), 627-637. https://doi.org/10.32322/jhsm.1870007

AMA

1.Kuşçu Aİ, Ortataş FN, Dolar A, Kuşçu S. AI-based determination of Kennedy classification and modification spaces on panoramic radiographs. J Health Sci Med / JHSM. 2026;9(3):627-637. doi:10.32322/jhsm.1870007

Chicago

Kuşçu, Aliye İpek, Fatma Nur Ortataş, Ayça Dolar, and Süha Kuşçu. 2026. “AI-Based Determination of Kennedy Classification and Modification Spaces on Panoramic Radiographs”. Journal of Health Sciences and Medicine 9 (3): 627-37. https://doi.org/10.32322/jhsm.1870007.

EndNote

Kuşçu Aİ, Ortataş FN, Dolar A, Kuşçu S (May 1, 2026) AI-based determination of Kennedy classification and modification spaces on panoramic radiographs. Journal of Health Sciences and Medicine 9 3 627–637.

IEEE

[1]A. İ. Kuşçu, F. N. Ortataş, A. Dolar, and S. Kuşçu, “AI-based determination of Kennedy classification and modification spaces on panoramic radiographs”, J Health Sci Med / JHSM, vol. 9, no. 3, pp. 627–637, May 2026, doi: 10.32322/jhsm.1870007.

ISNAD

Kuşçu, Aliye İpek - Ortataş, Fatma Nur - Dolar, Ayça - Kuşçu, Süha. “AI-Based Determination of Kennedy Classification and Modification Spaces on Panoramic Radiographs”. Journal of Health Sciences and Medicine 9/3 (May 1, 2026): 627-637. https://doi.org/10.32322/jhsm.1870007.

JAMA

1.Kuşçu Aİ, Ortataş FN, Dolar A, Kuşçu S. AI-based determination of Kennedy classification and modification spaces on panoramic radiographs. J Health Sci Med / JHSM. 2026;9:627–637.

MLA

Kuşçu, Aliye İpek, et al. “AI-Based Determination of Kennedy Classification and Modification Spaces on Panoramic Radiographs”. Journal of Health Sciences and Medicine, vol. 9, no. 3, May 2026, pp. 627-3, doi:10.32322/jhsm.1870007.

Vancouver

1.Aliye İpek Kuşçu, Fatma Nur Ortataş, Ayça Dolar, Süha Kuşçu. AI-based determination of Kennedy classification and modification spaces on panoramic radiographs. J Health Sci Med / JHSM. 2026 May 1;9(3):627-3. doi:10.32322/jhsm.1870007