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1. Use of artificial intelligence to detect dental caries on intraoral photos

   Zeng, Z; Ramesh, A; Ruan, J; Hao, P; Al_Jallad, N; Jang, H; Ly-Mapes, O; Fiscella, K; Xiao, J; Luo, J (January 2025, Quintessence international)
2. Targeting S. mutans biofilms: a perspective on preventing dental caries

   https://doi.org/10.1039/c9md00015a  

   Scharnow, Amber M.; Solinski, Amy E.; Wuest, William M. (July 2019, MedChemComm)

   The prevalence of biofilm diseases, and dental caries in particular, have encouraged extensive research on S. mutans biofilms, including methods of preventing its formation. Numerous small molecules with specific anti-biofilm activity against this pathogen have been isolated and synthesized. Generally, these molecules can be characterized into three categories: sucrose-dependent anti-adhesion, sucrose-independent anti-adhesion and cellular signaling interference. This review aims to provide an overview of the current small molecule strategies used for targeting S. mutans biofilms, and a perspective of the future for the field. 

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3. Near-Infrared Imaging of Artificial Enamel Caries Lesions with a Scanning Fiber Endoscope

   https://doi.org/10.3390/s19061419  

   Lee, Robert; Zhou, Yaxuan; Finkleman, Sara; Sadr, Alireza; Seibel, Eric (March 2019, Sensors)

   Several studies have shown that near-infrared imaging has great potential for the detection of dental caries lesions. A miniature scanning fiber endoscope (SFE) operating at near-infrared (NIR) wavelengths was developed and used in this study to test whether the device could be used to discriminate demineralized enamel from sound enamel. Varying depths of artificial enamel caries lesions were prepared on 20 bovine blocks with smooth enamel surfaces. Samples were imaged with a SFE operating in the reflectance mode at 1310-nm and 1460-nm in both wet and dry conditions. The measurements acquired by the SFE operating at 1460-nm show significant difference between the sound and the demineralized enamel. There was a moderate positive correlation between the SFE measurements and micro-CT measurements, and the NIR SFE was able to detect the presence of demineralization with high sensitivity (0.96) and specificity (0.85). This study demonstrates that the NIR SFE can be used to detect early demineralization from sound enamel. In addition, the NIR SFE can differentiate varying severities of demineralization. With its very small form factor and maneuverability, the NIR SFE should allow clinicians to easily image teeth from multiple viewing angles in real-time. 

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4. Optimization methods for deep neural networks classifying OCT images to detect dental caries

   https://doi.org/10.1117/12.2545421  

   Salehi, Hassan S.; Barchini, Majd; Mahdian, Mina (February 2020, SPIE BIOS, Lasers in Dentistry XXVI)

   Rechmann, Peter; Fried, Daniel (Ed.)
5. Machine Learning Approach Identified Multi-Platform Factors for Caries Prediction in Child-Mother Dyads

   https://doi.org/10.3389/fcimb.2021.727630  

   Wu, Tong Tong; Xiao, Jin; Sohn, Michael B.; Fiscella, Kevin A.; Gilbert, Christie; Grier, Alex; Gill, Ann L.; Gill, Steve R. (August 2021, Frontiers in Cellular and Infection Microbiology)

   Untreated tooth decays affect nearly one third of the world and is the most prevalent disease burden among children. The disease progression of tooth decay is multifactorial and involves a prolonged decrease in pH, resulting in the demineralization of tooth surfaces. Bacterial species that are capable of fermenting carbohydrates contribute to the demineralization process by the production of organic acids. The combined use of machine learning and 16s rRNA sequencing offers the potential to predict tooth decay by identifying the bacterial community that is present in an individual’s oral cavity. A few recent studies have demonstrated machine learning predictive modeling using 16s rRNA sequencing of oral samples, but they lack consideration of the multifactorial nature of tooth decay, as well as the role of fungal species within their models. Here, the oral microbiome of mother–child dyads (both healthy and caries-active) was used in combination with demographic–environmental factors and relevant fungal information to create a multifactorial machine learning model based on the LASSO-penalized logistic regression. For the children, not only were several bacterial species found to be caries-associated ( Prevotella histicola, Streptococcus mutans , and Rothia muciloginosa ) but also Candida detection and lower toothbrushing frequency were also caries-associated. Mothers enrolled in this study had a higher detection of S. mutans and Candida and a higher plaque index. This proof-of-concept study demonstrates the significant impact machine learning could have in prevention and diagnostic advancements for tooth decay, as well as the importance of considering fungal and demographic–environmental factors. 

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