Classificação Ordinal de Lesões de Cárie Cavitadas e Não Cavitadas em Fotografias da Superfície Oclusal Baseada no ICDAS com Transfer Learning
Resumo
A subjetividade do diagnóstico visual da cárie compromete a reprodutibilidade clínica. Este estudo avaliou a classificação ordinal da severidade (ICDAS) em 356 imagens, utilizando Transfer Learning com dez arquiteturas convolucionais pré-treinadas no ImageNet e decomposição de Frank e Hall. Comparou-se a extração estática associada a modelos clássicos (SVM, MLP e RF) com o ajuste fino parcial. O comportamento preditivo variou conforme a complexidade arquitetural: redes profundas, como EfficientNetV2B3, exigiram a abordagem híbrida, enquanto as clássicas, como VGG19, otimizaram-se com ajuste fino (ambas com QWK ≈ 0,84; F1-Score > 81%). A convergência preditiva dessas estratégias atesta a viabilidade do telediagnóstico odontológico.Referências
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Alabd-Aljabar, A., Raisan, Z., Adnan, M., and Dhou, S. (2024). A hybrid transfer learning approach to teeth diagnosis using orthopantomogram radiographs. IEEE Access, 12:178142–178152.
Bader, J. D. and Shugars, D. A. (2006). The evidence supporting alternative management strategies for early occlusal caries and suspected occlusal dentinal caries. Journal of Evidence Based Dental Practice, 6(1):91–100.
Bernabe, E., Marcenes, W., Abdulkader, R. S., et al. (2025). Trends in the global, regional, and national burden of oral conditions from 1990 to 2021: a systematic analysis for the global burden of disease study 2021. The Lancet, 405(10482):897–910.
Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
Cheng, J., Wang, Z., and Pollastri, G. (2008). A neural network approach to ordinal regression. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pages 1279–1284.
Cohen, J. (1968). Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological Bulletin, 70(4):213–220.
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3):273–297.
Duong, D. L., Kabir, M. H., and Kuo, R. F. (2021). Automated caries detection with smartphone color photography using machine learning. Health Informatics Journal, 27(2):14604582211007530.
Frank, E. and Hall, M. (2001). A simple approach to ordinal classification. In De Raedt, L. and Flach, P., editors, Machine Learning: ECML 2001, pages 145–156, Berlin, Heidelberg. Springer Berlin Heidelberg.
Frencken, J. E. (2017). Atraumatic restorative treatment and minimal intervention dentistry. British Dental Journal, 223(3):183–189.
Gimenez, T., Piovesan, Carmel e Braga, M. M., Ricketts, D. N., and Mendes, F. M. (2015). Visual inspection for caries detection: a systematic review and meta-analysis. Journal of Dental Research, 94(7):895–904.
He, K., Zhang, X., Ren, S., and Sun, J. (2016a). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778.
He, K., Zhang, X., Ren, S., and Sun, J. (2016b). Identity mappings in deep residual networks. In European Conference on Computer Vision, pages 630–645. Springer.
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., Le, Q. V., and Adam, H. (2019). Searching for mobilenetv3. In Proceedings of the IEEE International Conference on Computer Vision, pages 1314–1324.
Ismail, A. I., Sohn, W., Tellez, M., Amaya, A., Sen, A., Hasson, H., and Pitts, N. B. (2007). The international caries detection and assessment system (icdas): an integrated system for measuring dental caries. Community Dentistry and Oral Epidemiology, 35(3):170–178.
Jolliffe, I. T. (2002). Principal Component Analysis. Springer, New York, NY, 2nd edition.
Kohara, E. K., Abdala, C. G., Novaes, T. F., Braga, M. M., Haddad, A. E., and Mendes, F. M. (2018). Is it feasible to use smartphone images to perform telediagnosis of different stages of occlusal caries lesions? PLoS ONE, 13(9):e0202116.
Krothapalli, N. and Cherukumalli Kapalavayi, N. (2025). Deep learning in dental diagnostics: Caries detection through smartphone photographs – a systematic review. Journal of Global Oral Health, 8:91–97.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444.
Lee, D.-H., Li, Y., and Shin, B.-S. (2020). Mid-level feature extraction method based transfer learning to small-scale dataset of medical images with visualizing analysis. Journal of Information Processing Systems, 16(6):1293–1308.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., and Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42:60–88.
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022). A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11966–11976.
Pitts, N. B. and Ekstrand, K. R. (2013). International caries detection and assessment system (icdas) and its international caries classification and management system (iccms): methods for staging of the caries process and enabling dentists to manage caries. Community Dentistry and Oral Epidemiology, 41:e41–e52.
Razavian, A. S., Azizpour, H., Sullivan, J., and Carlsson, S. (2014). Cnn features off-the-shelf: An astounding baseline for recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 512–519.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088):533–536.
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR).
Sokolova, M. and Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information processing & management, 45(4):427–437.
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the AAAI Conference on Artificial Intelligence.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2818–2826.
Tan, M. and Le, Q. V. (2021). Efficientnetv2: Smaller models and faster training. In Proceedings of the International Conference on Machine Learning (ICML), pages 10096–10106.
Tareq, A., Faisal, M. I., Islam, M. S., Rafa, N. S., Chowdhury, T., Ahmed, S., Farook, T. H., Mohammed, N., and Dudley, J. (2023). Visual diagnostics of dental caries through deep learning of non-standardised photographs using a hybrid yolo ensemble and transfer learning model. International Journal of Environmental Research and Public Health, 20(7):5351.
Publicado
01/06/2026
Como Citar
DANTAS, Ana Larissa Teixeira; CUNHA, Jadiel Silva da; PEREIRA, Julyana Raab; NEVES, Beatriz Gonçalves; SILVA, Bruno Riccelli dos Santos; MANSO, Adriana Pigozzo; FRANCO, Wellington; RODRIGUES, Lidiany Karla Azevedo.
Classificação Ordinal de Lesões de Cárie Cavitadas e Não Cavitadas em Fotografias da Superfície Oclusal Baseada no ICDAS com Transfer Learning. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 788-799.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2026.21521.
