Performance evaluation of deep learning models for overbite classification on cephalometric radiographs

Sultan Büşra Ay Kartbak; Mehmet Birol Özel; Muhammet Çakmak

doi:10.26650/eor.20251689033

Performance evaluation of deep learning models for overbite classification on cephalometric radiographs

Abstract

Purpose The objective of this study was to evaluate and compare the effectiveness of different deep learning algorithms in classifying overbite based on lateral cephalometric radiographic images.

Materials and Methods This study was conducted using lateral cephalometric radiographs of 1062 patients. Overbite values were measured via WebCeph, and the radiographs were categorized into three groups (Overbite 1, Overbite 2, and Overbite 3) based on cephalometric measurements. Six deep learning models (ResNet101, DenseNet201, EfficientNetV2-B0, ConvNetBase, EfficientNet-B0, and a Hybrid Model) were employed to classify the radiographs. Model performance was evaluated using various metrics, including F1-score, accuracy, precision, recall, mean absolute error (MAE), Cohen’s Kappa coefficient, and area under the ROC curve (AUC-ROC). Additionally, confusion matrices and Grad-CAM visualizations were generated to further interpret the models’ decision-making processes.

Results All deep learning models employed in this study achieved classification accuracies exceeding 85%. Among them, the EfficientNet B0 and Hybrid models yielded the highest accuracy rates, whereas the ConvNetBase model demonstrated the lowest performance in terms of classification accuracy.

Conclusion The findings of this study highlight the potential of deep learning models to accurately and reliably classify cephalometric overbite categories without the need for conventional cephalometric analysis.

Keywords

References

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Details

Primary Language

English

Subjects

Oral and Maxillofacial Radiology, Orthodontics and Dentofacial Orthopaedics

Journal Section

Research Article

Authors

Sultan Büşra Ay Kartbak
0000-0003-3862-5658
Türkiye

Mehmet Birol Özel ^*
0000-0002-2984-9468
Türkiye

Muhammet Çakmak
1026-6502-0251-6890
Türkiye

Publication Date

May 20, 2026

Submission Date

May 1, 2025

Acceptance Date

June 26, 2025

Published in Issue

Year 2026 Volume: 60 Number: 1

DOI

https://doi.org/10.26650/eor.20251689033

IZ

https://izlik.org/JA88EN29ML

Cite

RIS / Bibtex

APA

Ay Kartbak, S. B., Özel, M. B., & Çakmak, M. (2026). Performance evaluation of deep learning models for overbite classification on cephalometric radiographs. European Oral Research, 60(1), 92-97. https://doi.org/10.26650/eor.20251689033

AMA

1.Ay Kartbak SB, Özel MB, Çakmak M. Performance evaluation of deep learning models for overbite classification on cephalometric radiographs. EOR. 2026;60(1):92-97. doi:10.26650/eor.20251689033

Chicago

Ay Kartbak, Sultan Büşra, Mehmet Birol Özel, and Muhammet Çakmak. 2026. “Performance Evaluation of Deep Learning Models for Overbite Classification on Cephalometric Radiographs”. European Oral Research 60 (1): 92-97. https://doi.org/10.26650/eor.20251689033.

EndNote

Ay Kartbak SB, Özel MB, Çakmak M (May 1, 2026) Performance evaluation of deep learning models for overbite classification on cephalometric radiographs. European Oral Research 60 1 92–97.

IEEE

[1]S. B. Ay Kartbak, M. B. Özel, and M. Çakmak, “Performance evaluation of deep learning models for overbite classification on cephalometric radiographs”, EOR, vol. 60, no. 1, pp. 92–97, May 2026, doi: 10.26650/eor.20251689033.

ISNAD

Ay Kartbak, Sultan Büşra - Özel, Mehmet Birol - Çakmak, Muhammet. “Performance Evaluation of Deep Learning Models for Overbite Classification on Cephalometric Radiographs”. European Oral Research 60/1 (May 1, 2026): 92-97. https://doi.org/10.26650/eor.20251689033.

JAMA

1.Ay Kartbak SB, Özel MB, Çakmak M. Performance evaluation of deep learning models for overbite classification on cephalometric radiographs. EOR. 2026;60:92–97.

MLA

Ay Kartbak, Sultan Büşra, et al. “Performance Evaluation of Deep Learning Models for Overbite Classification on Cephalometric Radiographs”. European Oral Research, vol. 60, no. 1, May 2026, pp. 92-97, doi:10.26650/eor.20251689033.

Vancouver

1.Sultan Büşra Ay Kartbak, Mehmet Birol Özel, Muhammet Çakmak. Performance evaluation of deep learning models for overbite classification on cephalometric radiographs. EOR. 2026 May 1;60(1):92-7. doi:10.26650/eor.20251689033