Dental caries and non caries detection using deep learning
Keywords:
Cavities, dental caries, deep learningAbstract
Cavities are the most common indication of dental caries, a contagious condition that leads to the deterioration of the tooth's structure. Dental caries has been identified as one of the most common oral health issue. This research has been conducted to identify them early, owing to the discomfort and high expense of treatment. Artificial intelligence has been utilized in recent years to create models which can forecast the risk of dental caries due to restrictions in medical research in oral healthcare, such as the high costs and lengthy requirements. Data for our study were collected from Khyber College of Dentistry and Hospital. On this data, a number of Deep Learning algorithms were implemented, and their performances were evaluated using recall, precision, F1-score, and accuracy. In comparison to CNN, LeNet and AlexNet deep learning techniques, VGG16 has the best performance, scoring accuracy of 98.99%, F1-score of 0.96% with precision of 0.95%, and a recall of 0.97%. This suggested paper demonstrated that DL is strongly advised for dental professionals to use in helping them make decisions for the early diagnosis and treatment of dental caries.
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References
Dye, Bruce A. Oral health disparities as determined by selected healthy people 2020 oral health objectives for the United States, 2009-2010. No. 100. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, 2012.
Dye, Bruce A., et al. "Trends in oral health status; United States, 1988-1994 and 1999-2004." (2007).
Vos, Theo, et al. "Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016." The Lancet 390.10100 (2017): 1211-1259.
Casamassimo, Paul S., et al. "Beyond the dmft: the human and economic cost of early childhood caries." The Journal of the American Dental Association 140.6 (2009): 650-657.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
Schmidhuber, Jürgen. "Deep learning in neural networks: An overview." Neural networks 61 (2015): 85-117.
Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." nature 529.7587 (2016): 484-489.
Schmidhuber, Jürgen. "Deep learning in neural networks: An overview." Neural networks 61 (2015): 85-117.
Chen, X., Guo, J., Ye, J., Zhang, M., & Liang, Y. (2022). Detection of Proximal Caries Lesions on Bitewing Radiographs Using Deep Learning Method. Caries Research, 56(5–6), 455–463. https://doi.org/10.1159/000527418
Reyes, L. T., Knorst, J. K., Ortiz, F. R., & Ardenghi, T. M. (2022). Machine Learning in the Diagnosis and Prognostic Prediction of Dental Caries: A Systematic Review. Caries Research, 56(3), 161–170. https://doi.org/10.1159/000524167
Lussi, A., & Francescut, P. (2003). Performance of Conventional and New Methods for the Detection of Occlusal Caries in Deciduous Teeth. Caries Research, 37(1), 2–7. https://doi.org/10.1159/000068226
Malhotra, Y. (2018). AI, Machine Learning & Deep Learning Risk Management & Controls: Beyond Deep Learning and Generative Adversarial Networks: Model Risk Management in AI, Machine Learning & Deep Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3193693
Themes, U., & D. (2020, January 7). Personalized Dental Caries Management in Children. Personalized Dental Caries Management in Children | Pocket Dentistry. https://pocketdentistry.com/personalized-dental-caries-management-in-children/
Kang, I. A., Ngnamsie Njimbouom, S., Lee, K. O., & Kim, J. D. (2022, March 16). DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine. Applied Sciences, 12(6), 3043. https://doi.org/10.3390/app12063043
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