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Objective <p>The aim of this pilot study was to compare the performance of deep learning models in detecting and classifying the buccal, middle, and palatal orientations of impacted maxillary canines using panoramic radiographs.</p> Methods <p>A total of 200 panoramic radiographs were collected from patients’ records with a history of unilateral and/or bilateral impacted maxillary canines. The images were preprocessed for resizing, bit depth, and file format. Data augmentation was performed by horizontal flipping to increase the total dataset size from 200 to 400 images. The images were randomly divided into three subsets as 80% training, 10% validation and 10% testing. The annotation was made by two experienced dentists. The dataset was trained and tested using two state-of-the-art deep learning models based on the YOLO11x and YOLO12x architectures. Model performance was evaluated using standard metrics, including precision, recall, F1-score, mAP@0.5, and mAP@0.5:0.95. In addition, confusion matrices and visual predictions from the test images were analyzed to assess class-specific performance and localization accuracy.</p> Results <p>The YOLO11x model demonstrated superior performance compared to the YOLO12x model. During the training phase, the mAP@0.5 values were 0.723 and 0.639, respectively, while in the testing phase, they were 0.468 and 0.435, respectively. While both models achieved accurate localization in a limited number of test samples, the YOLO12x model exhibited a higher rate of false negatives. Visual inspection revealed that palatal orientations were classified more reliably, whereas classification performance was lower for buccal orientations and particularly limited for middle orientations.</p> Conclusion <p>This pilot study presented certain challenges in accurately classifying the spatial orientation of impacted maxillary canines due to methodological limitations (e.g., small sample size, preliminary nature). While satisfactory performance was achieved for palatal orientations, reduced classification accuracy and frequent misclassifications were observed for buccal and middle orientations. Given the inherent limitations of panoramic imaging, future studies incorporating larger datasets and 3D imaging techniques would be warranted to improve classification accuracy and clinical applicability.</p>

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Deep learning-based prediction of buccal, middle and palatal orientations of impacted maxillary canines using panoramic radiographs: a pilot study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Serpil Çokakoğlu,
  • Samet Tosun,
  • Muhammet Üsame Özi̇ç

摘要

Objective

The aim of this pilot study was to compare the performance of deep learning models in detecting and classifying the buccal, middle, and palatal orientations of impacted maxillary canines using panoramic radiographs.

Methods

A total of 200 panoramic radiographs were collected from patients’ records with a history of unilateral and/or bilateral impacted maxillary canines. The images were preprocessed for resizing, bit depth, and file format. Data augmentation was performed by horizontal flipping to increase the total dataset size from 200 to 400 images. The images were randomly divided into three subsets as 80% training, 10% validation and 10% testing. The annotation was made by two experienced dentists. The dataset was trained and tested using two state-of-the-art deep learning models based on the YOLO11x and YOLO12x architectures. Model performance was evaluated using standard metrics, including precision, recall, F1-score, mAP@0.5, and mAP@0.5:0.95. In addition, confusion matrices and visual predictions from the test images were analyzed to assess class-specific performance and localization accuracy.

Results

The YOLO11x model demonstrated superior performance compared to the YOLO12x model. During the training phase, the mAP@0.5 values were 0.723 and 0.639, respectively, while in the testing phase, they were 0.468 and 0.435, respectively. While both models achieved accurate localization in a limited number of test samples, the YOLO12x model exhibited a higher rate of false negatives. Visual inspection revealed that palatal orientations were classified more reliably, whereas classification performance was lower for buccal orientations and particularly limited for middle orientations.

Conclusion

This pilot study presented certain challenges in accurately classifying the spatial orientation of impacted maxillary canines due to methodological limitations (e.g., small sample size, preliminary nature). While satisfactory performance was achieved for palatal orientations, reduced classification accuracy and frequent misclassifications were observed for buccal and middle orientations. Given the inherent limitations of panoramic imaging, future studies incorporating larger datasets and 3D imaging techniques would be warranted to improve classification accuracy and clinical applicability.