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Improving accuracy of early dental carious lesions detection using deep learning-based automated method

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Abstract

Objective

To investigate the effectiveness of a convolutional neural network (CNN) in detecting healthy teeth and early carious lesions on occlusal surfaces and to assess the applicability of this deep learning algorithm as an auxiliary aid.

Materials and methods

A total of 2,481 posterior teeth (2,459 permanent and 22 deciduous teeth) with varying stages of carious lesions were classified according to the International Caries Detection and Assessment System (ICDAS). After clinical evaluation, ICDAS 0 and 2 occlusal surfaces were photographed with a professional digital camera. VGG-19 was chosen as the CNN and the findings were compared with those of a reference examiner to evaluate its detection efficiency. To verify the effectiveness of the CNN as an auxiliary detection aid, three examiners (an undergraduate student (US), a newly graduated dental surgeon (ND), and a specialist in pediatric dentistry (SP) assessed the acquired images (Phase I). In Phase II, the examiners reassessed the same images using the CNN-generated algorithms.

Results

The training dataset consisted of 8,749 images, whereas the test dataset included 140 images. VGG-19 achieved an accuracy of 0.879, positive agreement of 0.827, precision of 0.949, negative agreement 0.800, and an F1-score of 0.887. In Phase I, the accuracy rates for examiners US, ND, and SP were 0.543, 0.771, and 0.807, respectively. In Phase II, the accuracy rates improved to 0.679, 0.886, and 0.857 for the respective examiners. The number of correct answers was significantly higher in Phase II than in Phase I for all examiners (McNemar test;P<0.05).

Conclusions

VGG-19 demonstrated satisfactory performance in the detection of early carious lesions, as well as an auxiliary detection aid.

Clinical relevance

Automated detection using deep learning algorithms is an important aid in detecting early caries lesions and improves the accuracy of the disease detection, enabling quicker and more reliable clinical decision-making.

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Funding

This study was partially funded by the CAPES – Brazilian Federal Agency for Support and Evaluation of Graduate Education – Finance Code 001.

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Authors and Affiliations

  1. Stomatology Department, Universidade Federal do Paraná, Avenida Prefeito Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil

    Paula Dresch Portella, Bruna Cristine Dias, Juliana Feltrin de Souza & Luciana Reichert da Silva Assunção

  2. Informatic Department, Universidade Federal do Paraná, Curitiba, PR, Brazil

    Lucas Ferrari de Oliveira & Mateus Felipe de Cássio Ferreira

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  1. Paula Dresch Portella
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  2. Lucas Ferrari de Oliveira
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Contributions

Portella PD: Conceptualization, Methodology, Validation, Formal Analysis, Investigation, Resources, Data Curation, Writing - Original Draft. de Oliveira LF: Conceptualization, Methodology, Software, Formal Analysis, Writing - Review & Editing, Supervision. Ferreira MFC: Software, Formal Analysis. Dias BC: Investigation. de Souza JF: Investigation, Writing - Review & Editing. Assunção LRDS: Term, Conceptualization, Methodology, Validation, Formal Analysis, Investigation, Resources, Writing - Review & Editing, Project administration.

Corresponding author

Correspondence to Paula Dresch Portella.

Ethics declarations

Ethics approval

The study protocol was approved by the Human Research Ethics Committee of the Division of Health Sciences of the Universidade Federal do Paraná (UFPR) (CAAE 25001219.5.0000.0102).

Consent to participate

The consent form was signed by all participants (examiners) in the CNN testing phase.

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The authors declare no conflict of interest.

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Portella, P.D., de Oliveira, L.F., Ferreira, M.F. et al. Improving accuracy of early dental carious lesions detection using deep learning-based automated method. Clin Oral Invest 27, 7663–7670 (2023). https://doi.org/10.1007/s00784-023-05355-x

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