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Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm

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Abstract

Objectives

Artificial intelligence (AI) techniques like convolutional neural network (CNN) are a promising breakthrough that can help clinicians analyze medical imaging, diagnose taurodontism, and make therapeutic decisions. The purpose of the study is to develop and evaluate the function of CNN-based AI model to diagnose teeth with taurodontism in panoramic radiography.

Methods

434 anonymized, mixed-sized panoramic radiography images over the age of 13 years were used to develop automatic taurodont tooth segmentation models using a Pytorch implemented U-Net model. Datasets were split into train, validation, and test groups of both normal and masked images. The data augmentation method was applied to images of trainings and validation groups with vertical flip images, horizontal flip images, and both flip images. The Confusion Matrix was used to determine the model performance.

Results

Among the 43 test group images with 126 labels, there were 109 true positives, 29 false positives, and 17 false negatives. The sensitivity, precision, and F1-score values of taurodont tooth segmentation were 0.8650, 0.7898, and 0.8257, respectively.

Conclusions

CNN’s ability to identify taurodontism produced almost identical results to the labeled training data, and the CNN system achieved close to the expert level results in its ability to detect the taurodontism of teeth.

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Funding

This work has been supported by Eskişehir University Scientific Research Projects Coordination Unit under Grant number 202045E06.

Author information

Authors and Affiliations

  1. Department of Paediatric Dentistry, Faculty of Dentistry, Inonu University, Malatya, 44280, Turkey

    Sacide Duman

  2. Department of Endodontics, Clinic Dentplus, Bursa, Turkey

    Emir Faruk Yılmaz

  3. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey

    Gözde Eşer

  4. Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey

    Özer Çelik

  5. Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey

    Özer Çelik & Ibrahim Sevki Bayrakdar

  6. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey

    Ibrahim Sevki Bayrakdar & Elif Bilgir

  7. Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo, Brazil

    Andre Luiz Ferreira Costa

  8. Division of Oral & Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA

    Rohan Jagtap

  9. Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey

    Kaan Orhan

  10. Medical Design Application and Research Center (MEDITAM), Ankara University, Ankara, Turkey

    Kaan Orhan

Authors
  1. Sacide Duman
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  2. Emir Faruk Yılmaz
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  3. Gözde Eşer
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  4. Özer Çelik
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  5. Ibrahim Sevki Bayrakdar
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  7. Andre Luiz Ferreira Costa
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Contributions

SD, ISB, and EFY conceived the ideas; GE, SD, and EFY collected the data; OC, EB, and ISB analyzed the data; SD, ISB, and GE: writing—original draft; ALFC, RJ, and KO: supervision.

Corresponding author

Correspondence to Sacide Duman.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All the procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Informed consent was obtained from all the patients for being included in the study. This article does not contain any studies with human or animal subjects performed by the any of the author.

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Duman, S., Yılmaz, E.F., Eşer, G. et al. Detecting the presence of taurodont teeth on panoramic radiographs using a deep learning-based convolutional neural network algorithm. Oral Radiol 39, 207–214 (2023). https://doi.org/10.1007/s11282-022-00622-1

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  • DOI: https://doi.org/10.1007/s11282-022-00622-1

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