European oral research · 2026
Performance evaluation of deep learning models for overbite classification on cephalometric radiographs.
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Kartbak Sultan Büşra Ay, Özel Mehmet Birol, Çakmak Muhammet
Аннотация
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.
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.