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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Optical imaging of dental plaque pH
Tooth decay is one of the most common chronic infectious diseases worldwide. Bacteria from the oral biofilm create a local acidic environment that demineralizes the enamel in the caries disease process. By optically imaging plaque pH in pits and fissures and contacting surfaces of teeth, then medicinal therapies can be accurately applied to prevent or monitor the reversal of caries. To achieve this goal, the fluorescence emission from an aqueous solution of sodium fluorescein was measured using a multimodal scanning fiber endoscope (mmSFE). The 1.6-millimeter diameter mmSFE scans 424nm laser light and collects wide-field reflectance for navigational purposes in grayscale at 30 Hz. Two fluorescence channels centered at 520 and 549 nm are acquired and ratiometric analysis produces a pseudo-color overlay of pH. In vitro measurements calibrate the pH heat maps in the range 4.7 to 7.2 pH (0.2 standard deviation). In vivo measurements of a single case study provides informative images of interproximal biofilm before and after a sugar rinse. Post processing a time series of images provides a method that calculates the average pH changes of oral biofilm, replicating the Stephan Curve. These spatio-temporal records of oral biofilm pH can provide a new method of assessing the risk of tooth decay, guide the application of preventative therapies, and provide a quantitative monitor of overall oral health. The non-contact in vivo optical imaging of pH may be extended to measurements of wound healing, tumor environment, and other food processing surfaces since it relies on low power laser light and a US FDA approved dye.
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- Award ID(s):
- 1631146
- NSF-PAR ID:
- 10134083
- Date Published:
- Journal Name:
- SPIE Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling
- Volume:
- 11315
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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