A Deep Learning Approach for Classification of Dentinal Tubule Occlusions

References

• Addy, M. 2002. Dentine hypersensitivity: New perspectives on an old problem. International Dental Journal 52 (5):367–2600. doi:10.1002/j.1875-595x.2002.tb00936.x.
   Web of Science ®Google Scholar
• Addy, M. 2005. Tooth brushing, tooth wear and dentine hypersensitivity are they associated? International Dental Journal 55: 261–267 . doi:10.1111/j.1875-595X.2005.tb00063.x.
   PubMed Web of Science ®Google Scholar
• Albawi, S., T. Abed Mohammed, and S. Al-Zawi. 2017. “Understanding of a convolutional neural network.” In 2017 International Conference on Engineering and Technology (ICET) , Antalya, Turkey -. 10.1109/ICEngTechnol.2017.8308186.
   Google Scholar
• Alex, K., I. Sutskever, and G. E. Hinton. 2012. ”ImageNet classification with deep convolutional neural networks.”In 26th Annual Conference on Neural Information Processing Systems 2012, Lake Tahoe, Nevada, USA. 1, 1097–1105 .
   Google Scholar
• Badrigilan, S., S. Nabavi, A. Ali Abin, N. Rostampour, I. Abedi, A. Shirvani, and M. Ebrahimi Moghaddam. 2021. Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: A meta-analysis study. International Journal of Computer Assisted Radiology and Surgery 16 (4):529–42. doi:10.1007/s11548-021-02326-z.
   PubMed Web of Science ®Google Scholar
• Brännström, M., L. A. Lindén, and A. Aström. 1967. The hydrodynamics of the dental tubule and of pulp fluid. A discussion of its significance in relation to dentinal sensitivity. Caries Research 1 (4):4. doi:10.1159/000259530.
   Google Scholar
• Chen, C. L., A. Parolia, A. Pau, and I. C. Celerino De Moraes Porto. 2015, 1. Comparative evaluation of the effectiveness of desensitizing agents in dentine tubule occlusion using scanning electron microscopy. Australian Dental Journal 60 (1): 65–72. doi:10.1111/adj.12275.
   PubMed Web of Science ®Google Scholar
• Chollet, F. 2017. “Xception: Deep learning with depthwise separable convolutions.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA ,1800–1807 . doi: 10.1109/CVPR.2017.195.
   Google Scholar
• Ciocca, L., I. Gallina, E. Navacchia, P. Baldissara, and R. Scotti. 2007. A new method for quantitative analysis of dentinal tubules. Computers in Biology and Medicine 37 (3):3. doi:10.1016/j.compbiomed.2006.01.009.
   Web of Science ®Google Scholar
• Cireşan, D. C., A. Giusti, L. M. Gambardella, and J. Schmidhuber. 2013”Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks”In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, Nagoya, Japan. . Springer. 411–418. doi:10.1007/978-3-642-40763-5_51.
   Google Scholar
• Connor, S., and T. M. Khoshgoftaar. 2019. A survey on image data augmentation for Deep Learning. Journal of Big Data 6:1. doi:10.1186/s40537-019-0197-0.
   Google Scholar
• Costa, R. S. A., F. S. Rios, M. S. Moura, J. J. Jardim, M. Maltz, and A. N. Haas. 2014. Prevalence and risk indicators of dentin hypersensitivity in adult and elderly populations from Porto Alegre, Brazil. Journal of Periodontology 85 (9):1247–58. doi:10.1902/jop.2014.130728.
   PubMed Web of Science ®Google Scholar
• Cummins, D. 2010. Recent advances in dentin hypersensitivity: Clinically proven treatments for instant and lasting sensitivity relief. American Journal of Dentistry 23: Spec No A:3A–13A .
   Web of Science ®Google Scholar
• Deng, J., W. Dong, R. Socher, L. Li-Jia, L. Kai, and L. Fei-Fei. 2009. ”ImageNet: A large-scale hierarchical image database” In 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 248–255 .doi:10.1109/cvpr.2009.5206848.
   Google Scholar
• Esteva, A., B. Kuprel, R. A. Novoa, K. Justin, S. M. Swetter, H. M. Blau, and S. Thrun. 2017. Dermatologist-Level classification of skin cancer with deep neural networks. Nature 542 (7639):115–18. doi:10.1038/nature21056.
   PubMed Web of Science ®Google Scholar
• Fourcade, A., and R. H. Khonsari. 2019, 4. Deep learning in medical image analysis: A third eye for doctors. Journal of Stomatology, Oral and Maxillofacial Surgery 120 (4): 279–88. doi:10.1016/j.jormas.2019.06.002.
   PubMed Web of Science ®Google Scholar
• Fukushima, K., S. Miyake, and T. Ito. 1983. Neocognitron: A neural network model for a mechanism of visual pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics SMC-13 (5):826–34. doi:10.1109/TSMC.1983.6313076.
   Web of Science ®Google Scholar
• Fukushima, K. 1988. Neocognitron: A hierarchical neural network capable of visual pattern recognition. Neural Networks 1 (2):119–30. doi:10.1016/0893-6080(88)90014-7.
   Web of Science ®Google Scholar
• Girshick, R., J. Donahue, T. Darrell, and J. Malik. 2014. “Rich feature hierarchies for accurate object detection and semantic segmentation.” In 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 580–587 . 10.1109/CVPR.2014.81.
   Google Scholar
• Gulshan, V., L. Peng, M. Coram, M. C. Stumpe, W. Derek, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, et al. 2016 . Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA - Journal of the American Medical Association 316 22: 2402. 10.1001/jama.2016.17216.
   PubMed Web of Science ®Google Scholar
• Haibo, H., and E. A. Garcia. 2009. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21 (9):1263–84. doi:10.1109/TKDE.2008.239.
   Web of Science ®Google Scholar
• Howard, A. G., M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam. 2017. MobileNets: Efficient convolutional neural networks for mobile vision. CoRR abs/1704.04861 http://arxiv.org/abs/1704.04861.
   Google Scholar
• Hubel, D. H., and T. N. Wiesel. 1962. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology 160 (1):106–54. doi:10.1113/jphysiol.1962.sp006837.
   PubMed Web of Science ®Google Scholar
• Hubel, D. H., and T. N. Wiesel. 1968. Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology 195 (1):215–43. doi:10.1113/jphysiol.1968.sp008455.
   PubMed Web of Science ®Google Scholar
• Jones, S. B., C. R. Parkinson, P. Jeffery, M. Davies, E. L. Macdonald, J. Seong, and N. X. West. 2015. A randomised clinical trial investigating calcium sodium phosphosilicate as a dentine mineralising agent in the oral environment. Journal of Dentistry 43 (6):757–64. doi:10.1016/j.jdent.2014.10.005.
   PubMed Web of Science ®Google Scholar
• Kaiming, H., X. Zhang, S. Ren, and J. Sun. 2016. “Deep residual learning for image recognition.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 770–778 , . doi:10.1109/CVPR.2016.90.
   Google Scholar
• Ker, J., L. Wang, J. Rao, and T. Lim. 2017. Deep learning applications in medical image analysis. IEEE Access 6:1407–15. doi:10.1109/ACCESS.2017.2788044.
   PubMed Web of Science ®Google Scholar
• Kingma, D. P., and B. Jimmy 2015. “Adam: A method for stochastic optimization.” In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, San Diego, CA, USA.
   Google Scholar
• Kunam, D., S. Manimaran, V. Sampath, and M. Sekar. 2016. Evaluation of dentinal tubule occlusion and depth of penetration of nano-hydroxyapatite derived from chicken eggshell powder with and without addition of sodium fluoride: An in vitro study. Journal of Conservative Dentistry 19 (3):239–44. doi:10.4103/0972-0707.181940.
   PubMedGoogle Scholar
• LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-Based learning applied to document recognition. Proceedings of the IEEE 86 (11):2278–324. doi:10.1109/5.726791.
   Web of Science ®Google Scholar
• McHugh, M. L. 2012. Interrater reliability: The kappa statistic. Biochemistry Medicine 22:3. doi:10.11613/bm.2012.031.
   Web of Science ®Google Scholar
• Miglani, S., V. Aggarwal, and B. Ahuja. 2010. Dentin hypersensitivity: Recent trends in management. Journal of Conservative Dentistry 13 (4):218. doi:10.4103/0972-0707.73385.
   PubMedGoogle Scholar
• Olley, R. C., P. Pilecki, N. Hughes, P. Jeffery, R. S. Austin, R. Moazzez, and D. Bartlett. 2012. An in situ study investigating dentine tubule occlusion of dentifrices following acid challenge. Journal of Dentistry 40 (7):7. doi:10.1016/j.jdent.2012.03.008.
   Web of Science ®Google Scholar
• Olley, R. C., C. R. Parkinson, R. Wilson, R. Moazzez, and D. Bartlett. 2014. A novel method to quantify dentine tubule occlusion applied to in situ model samples. Caries Research 48 (1):1. doi:10.1159/000354654.
   PubMed Web of Science ®Google Scholar
• Pradeep, A. R., and A. Sharma. 2010. Comparison of clinical efficacy of a dentifrice containing calcium sodium phosphosilicate to a dentifrice containing potassium nitrate and to a placebo on dentinal hypersensitivity: A randomized clinical trial. Journal of Periodontology 81 (8):1167–73. doi:10.1902/jop.2010.100056.
   PubMed Web of Science ®Google Scholar
• Ren, S., H. Kaiming, R. Girshick, and J. Sun. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (6):6. doi:10.1109/TPAMI.2016.2577031.
   Web of Science ®Google Scholar
• Sermanet, P., D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. 2014. “Overfeat: Integrated recognition, localization and detection using convolutional networks.” In 2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings, Banff, Alberta, Canada.
   Google Scholar
• Simonyan, K., and A. Zisserman. 2015. “Very deep convolutional networks for large-scale image recognition.” In 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, San Diego, CA, USA.
   Google Scholar
• Tajbakhsh, N., J. Y. Shin, S. R. Gurudu, R. Todd Hurst, C. B. Kendall, M. B. Gotway, and J. Liang. 2016. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medical Imaging 35 (5):1299–312. doi:10.1109/TMI.2016.2535302.
   PubMed Web of Science ®Google Scholar
• Takada, K. 2016. Artificial intelligence expert systems with neural network machine learning may assist decision-making for extractions in orthodontic treatment planning. Journal of Evidence-Based Dental Practice 16 (3):190–92. doi:10.1016/j.jebdp.2016.07.002.
   PubMed Web of Science ®Google Scholar
• West, N. X., M. Sanz, A. Lussi, D. Bartlett, P. Bouchard, and D. Bourgeois. 2013 . Prevalence of dentine hypersensitivity and study of associated factors: A European population-based cross-sectional study. Journal of Dentistry 41 (10): 841–51. doi:10.1016/j.jdent.2013.07.017.
   PubMed Web of Science ®Google Scholar
• West, N. X., J. Seong, N. Hellin, E. L. Macdonald, S. B. Jones, and J. E. Creeth. 2018. Assessment of tubule occlusion properties of an experimental stannous fluoride toothpaste: A randomised clinical in situ study. Journal of Dentistry 76:125–31. doi:10.1016/j.jdent.2018.07.001.
   PubMed Web of Science ®Google Scholar
• Yang, X., S. Yong Yeo, J. Mei Hong, S. Thai Wong, W. Teng Tang, W. Zhen Zhou, G. Lee, et al. 2016. “A deep learning approach for tumor tissue image classification.” In 12th IASTED International Conference on Biomedical Engineering, BioMed 2016, Innsbruck, Austria. doi: 10.2316/P.2016.832-025.
   Google Scholar