Predictive models of dental caries based on Big Data and machine learning: a narrative review
DOI:
https://doi.org/10.61347/rcem.v2i1.e15Keywords:
Machine learning, Big Data, dental caries, artificial intelligence, predictive modelsAbstract
Dental caries is the most prevalent oral disease worldwide, affecting approximately 3.5 billion people and generating a significant impact on quality of life and healthcare systems. In response to this challenge, predictive models have emerged as key tools for early detection and prevention; however, many of them present methodological limitations, such as a high risk of bias and limited clinical applicability. In this context, advances in Big Data, artificial intelligence, and machine learning have driven the development of more accurate and efficient models. This narrative review aimed to analyze the scientific evidence on the application of these technologies in predicting the risk of dental caries, including studies published between 2015 and 2025 in databases such as Scopus, PubMed, and Web of Science. The results showed that convolutional neural networks, support vector machines, and artificial neural networks are the most used algorithms, standing out for their high diagnostic performance, with elevated values of accuracy and area under the curve. Radiographic images were the most frequently used type of data, followed by clinical data and mixed models. Nevertheless, important limitations were identified, including data heterogeneity, lack of standardized metrics, and limited external validation. In conclusion, artificial intelligence–based models show high potential to improve the prediction of dental caries, although strengthening methodological rigor and clinical validation is necessary to support their implementation in dental practice.
Downloads
References
Casalegno, F., Newton, T., Daher, R., Abdelaziz, M., Lodi-Rizzini, A., Schürmann, F., Krejci, I., & Markram, H. (2019). Caries detection with near-infrared transillumination using deep learning. Journal of Dental Research, 98(11), 1227–1233. https://doi.org/10.1177/0022034519871884
Dettori, M., Lamloum, D., Lingström, P., & Campus, G. (2025). Artificial intelligence and innovation in oral health care sciences: A conceptual review. Healthcare, 13(24), 3327. https://doi.org/10.3390/healthcare13243327
Ding, H., Wu, J., Zhao, W., Matinlinna, J., Burrow, M., & Tsoi, J. (2023). Artificial intelligence in dentistry—A review. Frontiers in Dental Medicine, 4, 1085251. https://doi.org/10.3389/fdmed.2023.1085251
Garcia, A., Gehrung, S., Krois, J., Chaurasia, A., Rossi, J., Gaudin, R., Elhennawy, K., & Schwendicke, F. (2020). Detecting caries lesions of different radiographic extension on bitewings using deep learning. Journal of Dentistry, 100, 103425. https://doi.org/10.1016/j.jdent.2020.103425
Havsed, K., Hänsel, G., Isberg, P., Pigg, M., Svensäter, G., Foresight Research Consortium, & Rohlin, M. (2023). Multivariable prediction models of caries increment: A systematic review and critical appraisal. Systematic Reviews, 12, 202. https://doi.org/10.1186/s13643-023-02298-y
Hung, M., Voss, M., Rosales, M., Li, W., Su, W., Xu, J., Bounsanga, J., Ruiz-Negrón, B., Lauren, E., & Licari, F. (2019). Application of machine learning for diagnostic prediction of root caries. Gerodontology, 36(4), 395–404. https://doi.org/10.1111/ger.12432
Khanagar, S., Al-Ehaideb, A., Maganur, P., Vishwanathaiah, S., Patil, S., Baeshen, H., Sarode, S., & Bhandi, S. (2021). Developments, application, and performance of artificial intelligence in dentistry – A systematic review. Journal of Dental Sciences, 16(1), 508–522. https://doi.org/10.1016/j.jds.2020.06.019
Krois, J., Ekert, T., Meinhold, L., Golla, T., Kharbot, B., Wittemeier, A., Dörfer, C, & Schwendicke (2019). Deep learning for the radiographic detection of periodontal bone loss. Scientific Reports, 9, 8495. https://doi.org/10.1038/s41598-019-44839-3
Lee, J., Kim, D., Jeong, S., & Choi, S. (2018). Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. Journal of Dentistry, 77, 106–111. https://doi.org/10.1016/j.jdent.2018.07.015
Park, W., & Park, J. (2018). History and application of artificial neural networks in dentistry. European Journal of Dentistry, 12(4), 594–601. https://doi.org/10.4103/ejd.ejd_325_18
Schwendicke, F., Elhennawy, K., Paris, S., Friebertshäuser, P., & Krois, J. (2020). Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study. Journal of Dentistry, 92, 103260. https://doi.org/10.1016/j.jdent.2019.103260
Schwendicke, F., Golla, T., Dreher, M., & Krois, J. (2019). Convolutional neural networks for dental image diagnostics: A scoping review. Journal of Dentistry, 91, 103226. https://doi.org/10.1016/j.jdent.2019.103226
Schwendicke, F., Samek, W., & Krois, J. (2020). Artificial intelligence in dentistry: Chances and challenges. Journal of Dental Research, 99(7), 769–774. https://doi.org/10.1177/0022034520915714
Shah, S. (2023). Caries and public health. Journal of Khyber College of Dentistry, 12(3). https://doi.org/10.33279/jkcd.v12i03.74
Wang, L., Xu, Y., Wang, W., & Lu, Y. (2025). Application of machine learning in dentistry: Insights, prospects and challenges. Applied Oral Sciences, 84, 145–154. https://journalofkcd.com/kcd/article/view/74
Wang, X., Zhang, P., Lu, H., Luo, D., Yang, D., Li, K., Qiu, S., Zeng, H., & Zeng, X. (2025). Risk prediction models for dental caries in children and adolescents: A systematic review and meta-analysis. BMJ Open, 15(3), e088253. https://doi.org/10.1136/bmjopen-2024-088253
World Health Organization. (2025). Sugars and dental caries. https://www.who.int/news-room/fact-sheets/detail/sugars-and-dental-caries
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Noelia Amaray Velastegui Almeida, Gonzalo Paúl Rodríguez Galarza
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
