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Effect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs

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Abstract

Objective

This study aims to investigate the effect of number of data on model performance, for the detection of tooth numbering problem on dental panoramic radiographs, with the help of image processing and deep learning algorithms.

Study Design

The data set consists of 3000 anonymous dental panoramic X-rays of adult individuals. Panoramic X-rays were labeled on the basis of 32 classes in line with the FDI tooth numbering system. In order to examine the relationship between the number of data used in image processing algorithms and model performance, four different datasets which include 1000, 1500, 2000 and 2500 panoramic X-rays, were used. The training of the models was carried out with the YOLOv4 algorithm and trained models were tested on a fixed test dataset with 500 data and compared based on F1 score, mAP, sensitivity, precision and recall metrics.

Results

The performance of the model increased as the number of data used during the training of the model increased. Therefore, the last model trained with 2500 data showed the highest success among all the trained models.

Conclusion

Dataset size is important for dental enumeration, and large samples should be considered as more reliable.

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Data availibility

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Author notes

  1. Semih Gülüm and Seçilay Kutal have contributed equally to this work and share first authorship.

  2. Kader Cesur Aydin and Gazi Akgün have contributed equally to this work and share senior authorship.

Authors and Affiliations

  1. AVL Research and Engineering, Abdurrahmangazi Mah. Ataturk Cad. No: 22 11/22 Kat: 6 Sultanbeyli, Istanbul, 34920, Turkey

    Semih Gülüm

  2. AREAL.ai, San Francisco, USA

    Seçilay Kutal

  3. School of Dentistry, Head of Department Dentomaxillofacial Radiology, Istanbul Medipol University, Ataturk Bulvarı No: 27 Unkapanı, Istanbul, 34083, Turkey

    Kader Cesur Aydin

  4. Technology Faculty RTE Campus, Marmara University, T1/203, Maltepe, Istanbul, 34854, Turkey

    Gazi Akgün

  5. School of Dentistry, Istanbul Medipol University, Goztepe Mah, Ataturk Cad, Beykoz, Istanbul, 34810, Turkey

    Aleyna Akdağ

Authors
  1. Semih Gülüm
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  2. Seçilay Kutal
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  3. Kader Cesur Aydin
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Contributions

KCA: conceptualization, methodology, ınvestigation, writing—original draft preparation, writing—reviewing and editing, visualization. SK: methodology, ınvestigation, software, validation, data curation, ınvestigation, writing—original draft preparation. SG: methodology, ınvestigation, software, validation, data curation, ınvestigation, writing—original draft preparation, resources. GA: methodology, project administration, supervision. AA: software, validation, resources.

Corresponding author

Correspondence to Kader Cesur Aydin.

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Gülüm, S., Kutal, S., Cesur Aydin, K. et al. Effect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs. Oral Radiol 39, 715–721 (2023). https://doi.org/10.1007/s11282-023-00689-4

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  • DOI: https://doi.org/10.1007/s11282-023-00689-4

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