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Detection of pulpal calcifications on bite-wing radiographs using deep learning

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A Correction to this article was published on 08 September 2023

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Abstract

Objectives

Pulpal calcifications are discrete hard calcified masses of varying sizes in the dental pulp cavity. This study is aimed at measuring the performance of the YOLOv4 deep learning algorithm to automatically determine whether there is calcification in the pulp chambers in bite-wing radiographs.

Materials and methods

In this study, 2000 bite-wing radiographs were collected from the faculty database. The oral radiologists labeled the pulp chambers on the radiographs as “Present” and “Absent” according to whether there was calcification. The data were randomly divided into 80% training, 10% validation, and 10% testing. The weight file for pulpal calcification was obtained by training the YOLOv4 algorithm with the transfer learning method. Using the weights obtained, pulp chambers and calcifications were automatically detected on the test radiographs that the algorithm had never seen. Two oral radiologists evaluated the test results, and performance criteria were calculated.

Results

The results obtained on the test data were evaluated in two stages: detection of pulp chambers and detection of pulpal calcification. The detection performance of pulp chambers was as follows: recall 86.98%, precision 98.94%, F1-score 91.60%, and accuracy 86.18%. Pulpal calcification “Absent” and “Present” detection performance was as follows: recall 86.39%, precision 85.23%, specificity 97.94%, F1-score 85.49%, and accuracy 96.54%.

Conclusion

The YOLOv4 algorithm trained with bite-wing radiographs detected pulp chambers and calcification with high success rates.

Clinical relevance

Automatic detection of pulpal calcifications with deep learning will be used in clinical practice as a decision support system with high accuracy rates in diagnosing dentists.

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Data availability

The data sets are available from the corresponding author on a reasonable request.

Change history

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Acknowledgements

None. This study was presented as an oral presentation at XVIII. ECDMFR (European Congress of Dentomaxillofacial Radiology) Congress (Virtual) on 8th-10th June, 2022.

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Author notes

  1. Yuce Fatma

    Present address: Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Okan University, Istanbul, Turkey

Authors and Affiliations

  1. Faculty of Technology Department of Biomedical Engineering, Pamukkale University, Denizli, Turkey

    Muhammet Üsame Öziç

  2. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey

    Melek Tassoker

Authors
  1. Yuce Fatma
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  2. Muhammet Üsame Öziç
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Contributions

FY and MT obtained the imaging data and performed the radiological examination. MÜÖ designed the method and conducted the deep learning work and statistical analyses. All authors approved the final version of the manuscript.

Corresponding author

Correspondence to Melek Tassoker.

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Competing interests

The authors declare no competing interests.

Ethics approval

This study was conducted at the Faculty of Dentistry, Necmettin Erbakan University, Department of Dentomaxillofacial Radiology, with the approval of the Ethics Committee (No. 12/94) and was performed according to the stipulations laid out by the Declaration of Helsinki.

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Not applicable.

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Yuce, F., Öziç, M.Ü. & Tassoker, M. Detection of pulpal calcifications on bite-wing radiographs using deep learning. Clin Oral Invest 27, 2679–2689 (2023). https://doi.org/10.1007/s00784-022-04839-6

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