Redes Neurais Convolucionais Aplicadas à Odontologia: Revisão Sistemática da Literatura
Resumo
Este artigo apresenta um mapeamento sistemático sobre o uso de redes neurais convolucionais na odontologia, com foco em aplicações clínicas a partir de exames de imagem entre 2015 e 2025. A análise abrangeu 2.600 documentos da base Scopus, sendo filtrados e categorizados por arquiteturas utilizadas, tarefas (classificação, segmentação e detecção), tipos de imagem e aplicações odontológicas. Os resultados mostram crescimento expressivo da produção científica, com destaque para tarefas de segmentação e uso de imagens tomográficas. As arquiteturas mais frequentes foram CNN genérica, YOLO, ResNet e VGG.Referências
Baldan, L. C. et al. (2021). Odontologia durante a pandemia de covid-19. Vigilância Sanitária em Debate, 9(1):36–46.
Bornstein, M. M., Horner, K., and Jacobs, R. (2016). Use of cone beam computed tomography in implant dentistry: current concepts, indications and limitations for clinical practice and research. Periodontology 2000, 73(1):51–72.
Brasil. Ministério da Saúde (2011). Projeto SBBrasil 2010: pesquisa nacional de saúde bucal: resultados principais. Ministério da Saúde, Brasília, DF.
Francis, J. R. and Siu, T. L. (2023). Utility of cone beam imaging in periodontics and implant therapy. Decisions in Dentistry, ?(?):? Review article.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4700–4708.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42:60–88.
Lundervold, A. S. and Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on mri. Zeitschrift für Medizinische Physik, 29(2):102–127.
Nasseh, I. and Al-Rawi, W. (2018). Cone beam computed tomography. Dental Clinics of North America, 62(3):361–391.
Pereira, S. A., Corte-Real, A., Melo, A., Magalhães, L., Lavado, N., and Santos, J. M. (2024). Diagnostic accuracy of cone beam computed tomography and periapical radiography for detecting apical root resorption in retention phase of orthodontic patients: A cross-sectional study. Journal of Clinical Medicine, 13(5):1248.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pages 779–788.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
Silva, F. C. d., Bezerra, I. S. Q., Rebellato, N. L. B., and Lima, A. A. S. (2014). Cone beam computed tomography and applicability in dentistry – literature review. Revista Sul-Brasileira de Odontologia, 10(3):272–277.
Singh, P. K. et al. (2020). Convolutional neural networks in oral and maxillofacial radiology: A review. Imaging Science in Dentistry, 50(3):169–175.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
Tan, M. and Le, Q. V. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning, pages 6105–6114.
White, S. C. and Pharoah, M. J. (2015). White & Pharoah: princípios de interpretação radiográfica. Elsevier, Rio de Janeiro, 7 edition.
Bornstein, M. M., Horner, K., and Jacobs, R. (2016). Use of cone beam computed tomography in implant dentistry: current concepts, indications and limitations for clinical practice and research. Periodontology 2000, 73(1):51–72.
Brasil. Ministério da Saúde (2011). Projeto SBBrasil 2010: pesquisa nacional de saúde bucal: resultados principais. Ministério da Saúde, Brasília, DF.
Francis, J. R. and Siu, T. L. (2023). Utility of cone beam imaging in periodontics and implant therapy. Decisions in Dentistry, ?(?):? Review article.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4700–4708.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42:60–88.
Lundervold, A. S. and Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on mri. Zeitschrift für Medizinische Physik, 29(2):102–127.
Nasseh, I. and Al-Rawi, W. (2018). Cone beam computed tomography. Dental Clinics of North America, 62(3):361–391.
Pereira, S. A., Corte-Real, A., Melo, A., Magalhães, L., Lavado, N., and Santos, J. M. (2024). Diagnostic accuracy of cone beam computed tomography and periapical radiography for detecting apical root resorption in retention phase of orthodontic patients: A cross-sectional study. Journal of Clinical Medicine, 13(5):1248.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pages 779–788.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer.
Silva, F. C. d., Bezerra, I. S. Q., Rebellato, N. L. B., and Lima, A. A. S. (2014). Cone beam computed tomography and applicability in dentistry – literature review. Revista Sul-Brasileira de Odontologia, 10(3):272–277.
Singh, P. K. et al. (2020). Convolutional neural networks in oral and maxillofacial radiology: A review. Imaging Science in Dentistry, 50(3):169–175.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
Tan, M. and Le, Q. V. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning, pages 6105–6114.
White, S. C. and Pharoah, M. J. (2015). White & Pharoah: princípios de interpretação radiográfica. Elsevier, Rio de Janeiro, 7 edition.
Publicado
22/09/2025
Como Citar
RODRIGUES, Maria Isabelly de Brito; SOUSA, Raila Moura de; LIMA, Bruno Vicente Alves de; SANTOS, Iallen Gabio de Sousa.
Redes Neurais Convolucionais Aplicadas à Odontologia: Revisão Sistemática da Literatura. In: CONGRESSO DE DESENVOLVIMENTO E CIÊNCIA DA COMPUTAÇÃO (CODEC), 1. , 2025, Piripiri/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 39-46.
