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A system for automatic classification of endodontic treatment quality in CBCT

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Abstract

Objectives

An evaluation of the effectiveness of a new computational system proposed for automatic classification, developed based on a Siamese network combined with Convolutional Neural Networks (CNNs), is presented. It aims to identify endodontic technical errors using Cone Beam Computed Tomography (CBCT). The study also aims to compare the performance of the automatic classification system with that of dentists.

Methods

One thousand endodontically treated maxillary molars sagittal and coronal reconstructions were evaluated for the quality of the endodontic treatment and the presence of periapical hypodensities by three board-certified dentists and by an oral and maxillofacial radiologist. The proposed classification system was based on a Siamese network combined with EfficientNet B1 or EfficientNet B7 networks. Accuracy, sensivity, precision, specificity, and F1-score values were calculated for automated artificial systems and dentists. Chi-square tests were performed.

Results

The performances were obtained for EfficienteNet B1, EfficientNet B7 and dentists. Regarding accuracy, sensivity and specificity, the best results were obtained with EfficientNet B1. Concerning precision and F1-score, the best results were obtained with EfficientNet B7. The presence of periapical hypodensity lesions was associated with endodontic technical errors. In contrast, the absence of endodontic technical errors was associated with the absence of hypodensity.

Conclusions

Quality evaluation of the endodontic treatment performed by dentists and by Siamese Network combined with EfficientNet B7 or EfficientNet B1 networks was comparable with a slight superiority for the Siamese Network.

Clinical relevance

CNNs have the potential to be used as a support and standardization tool in assessing endodontic treatment quality in clinical practice.

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Availability of data and materials

The database used in this work is available upon request at https://github.com/felipebsferreira/periapical-lesion-dataset, with the recommendations for use and application, which was presented in [15]. In this work, the classification of the quality of endodontic treatment was added to the database, which is available with this update at the same link.

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Acknowledgements

The authors would like to thank Fundação de Amparo a Ciência e Tecnologia do Estado de Pernambuco (FACEPE) for the financial support, and the Department of Clinical and Preventive Dentistry of UFPE for supporting this research by providing the database.

Funding

This research was funded by FACEPE under grant number IBPG-0767-3.04/20.

Author information

Authors and Affiliations

  1. Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife, 50720-001, Pernambuco, Brasil

    Maria Alice Andrade Calazans, Maria de Lourdes Melo Guedes Alcoforado & Francisco Madeiro

  2. Departamento de Clínica e Odontologia Preventiva, Universidade Federal de Pernambuco, Recife, 50670-420, Pernambuco, Brasil

    Andréa dos Anjos Pontual, Maria Luíza dos Anjos Pontual, Andrezza Santos & Flávia Maria de Moraes Ramos-Perez

  3. Unidade Acadêmica do Cabo de Santo Agostinho, Universidade Federal Rural de Pernambuco, Cabo de Santo Agostinho, 54518-430, Pernambuco, Brasil

    Felipe Alberto B. S. Ferreira

Authors
  1. Maria Alice Andrade Calazans
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  2. Andréa dos Anjos Pontual
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  3. Maria Luíza dos Anjos Pontual
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  4. Felipe Alberto B. S. Ferreira
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  6. Maria de Lourdes Melo Guedes Alcoforado
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Contributions

Conceptualization: M. Calazans A. Pontual and F. Madeiro; methodology: M. Calazans, F. Ferreira A. Pontual, M. Pontual and F. Madeiro; software: M. Calazans and F. Ferreira; validation: F. Ferreira, A. Pontual, M. Pontual and F. Madeiro; formal analysis: A. Pontual and M. Pontual; investigation: M. Calazans, F. Ferreira A. Pontual, M. Pontual and F. Madeiro; resources: A. Santos, A. Pontual and M. Pontual; data curation: A. Santos and A. Pontual; writing—original draft preparation: M. Calazans and A. Pontual; writing—review and editing: M. Calazans, F. Ferreira, F. Madeiro, A. Santos, M. Alcoforado, A. Pontual, M. Pontual and F. Ramos-Perez; visualization: M. Calazans; supervision: M. Alcoforado, A. Pontual and F. Madeiro; project administration: F. Madeiro; funding acquisition: F. Madeiro. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Maria Alice Andrade Calazans.

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The work was submitted and approved by the Research Ethics Committee of the University of Pernambuco, Brazil, under certificate number 4.881.124.

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The authors declare no conflict of interest.

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Calazans, M.A.A., Pontual, A.d.A., Pontual, M.L.d.A. et al. A system for automatic classification of endodontic treatment quality in CBCT. Clin Oral Invest 28, 223 (2024). https://doi.org/10.1007/s00784-024-05599-1

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