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Objectives <p>Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture.</p> Materials and methods <p>The first stage involved tooth localization with the YOLOv8 model, followed by pulp stone classification using ResNeXt. 375 panoramic images were included in this study, and a comprehensive set of evaluation metrics, including precision, recall, false-negative rate, false-positive rate, accuracy, and F1 score was employed to rigorously assess the performance of the proposed architecture.</p> Results <p>Despite the limited annotated training data, the proposed method achieved impressive results: an accuracy of 95.4%, precision of 97.1%, recall of 96.1%, false-negative rate of 3.9%, false-positive rate of 6.1%, and a F1 score of 96.6%, outperforming existing approaches in pulp stone detection.</p> Conclusions <p>Unlike current studies, this approach adopted a more realistic scenario by utilizing a small dataset with few annotated samples, acknowledging the time-consuming and error-prone nature of expert labeling. The proposed system is particularly beneficial for dental students and newly graduated dentists who lack sufficient clinical experience, as it aids in the automatic detection of pulpal calcifications. To the best of our knowledge, this is the first study in the literature that propose a pipeline architecture to address the PS detection tasks on panoramic images.</p>

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A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs

  • 期刊论文

  • 发表日期：

    2025年1月14日

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  • Ceyda Gürhan,
  • Hasan Yiğit,
  • Selim Yılmaz,
  • Cihat Çetinkaya

摘要

Objectives

Pulp stones are ectopic calcifications located in pulp tissue. The aim of this study is to introduce a novel method for detecting pulp stones on panoramic radiography images using a deep learning-based two-stage pipeline architecture.

Materials and methods

The first stage involved tooth localization with the YOLOv8 model, followed by pulp stone classification using ResNeXt. 375 panoramic images were included in this study, and a comprehensive set of evaluation metrics, including precision, recall, false-negative rate, false-positive rate, accuracy, and F1 score was employed to rigorously assess the performance of the proposed architecture.

Results

Despite the limited annotated training data, the proposed method achieved impressive results: an accuracy of 95.4%, precision of 97.1%, recall of 96.1%, false-negative rate of 3.9%, false-positive rate of 6.1%, and a F1 score of 96.6%, outperforming existing approaches in pulp stone detection.

Conclusions

Unlike current studies, this approach adopted a more realistic scenario by utilizing a small dataset with few annotated samples, acknowledging the time-consuming and error-prone nature of expert labeling. The proposed system is particularly beneficial for dental students and newly graduated dentists who lack sufficient clinical experience, as it aids in the automatic detection of pulpal calcifications. To the best of our knowledge, this is the first study in the literature that propose a pipeline architecture to address the PS detection tasks on panoramic images.