Prediction of dental caries in 12-year-old children using machine-learning algorithms / 대한구강보건학회지

OBJECTIVES: The decayed-missing-filled (DMFT) index is a representative oral health indicator. Prediction of DMFT index is an important basis for the development of public oral health care projects and strategies for caries prevention. In this study, we used data from the 2015 Korean children's oral health survey to predict DMFT index and caries risk groups using statistical techniques and four different machine-learning algorithms.METHODS: DMFT prediction models were constructed using multiple linear regression and four different machine-learning algorithms: decision tree regressor, decision tree classifier (DTC), random forest regressor, and random forest classifier (RFC). Thereafter, their accuracies were compared.RESULTS: For the DMFT predictive model, the prediction accuracy of multiple linear regression and RFC were 15.24% and 43.27%, respectively. The accuracy of DTC prediction was 2.84 times that of multiple linear regression. The important feature of the machine-learning model, which predicts DMFT index and the caries risk group, was the number of teeth with sealants.CONCLUSIONS: Using data from the 2015 Korean children's oral health survey, which is considered big data in the field of oral health survey in Korea, this study confirmed that machine-learning models are more useful than statistical models for predicting DMFT index and caries risk in 12-year-old children. Therefore, it is expected that the machine-learning model can be used to predict the DMFT score.

Comparison of traditional dental plaque indices with real stained plaque area / 대한구강보건학회지

OBJECTIVES: The aim of this study was to compare Plaque Percent Index (PPI), calculated by Patient Hygiene Performance Index (PHPI), Rustogi's modification of the Navy Plaque Index (RMNPI), and the Quigley & Hein Plaque Index (QHPI), with visual assessment. METHODS: Ninety-six subjects, aged between 30–65 years, were examined; twenty subjects were included in the final analysis. The subjects' teeth were stained and photographed. Dental coloring and intraoral camera photography were performed by a single examiner. The oral images obtained were analyzed using Image J to measure the area of dental plaque. The values of PHPI, RMNPI, and QHPI were calculated twice. Statistical analyses were performed using descriptive statistics, chi-square test, and Pearson's correlation coefficient. RESULTS: The results of the correlation analyses of PPI with PHPI, QHPI, and RMNPI were as follows: for PHPI, the correlation coefficient (r)=0.584; for QHPI, r=0.689; and for RMNPI, r=0.729. Further, the kappa indices of PHPI, QHPI, and RMNPI were 0.810, 0.677, and 0.590 respectively. CONCLUSIONS: Among RMNPI, QHPI, and PHPI dental plaque indices, RMNPI and QHPI showed a high degree of correlation with the actual stained dental plaque area; on the other hand, PHPI showed the highest kappa index.

Red fluorescence of oral bacteria is affected by blood in the growth medium / 대한구강보건학회지

OBJECTIVES: Dental plaque emits red fluorescence under a visible blue light near the ultra-violet end of the light spectrum. The fluorescence characteristics of each microorganism have been reported in several studies. The aim of this study was to evaluate changes in red fluorescence of oral microorganisms that is affected by blood in the culture media. METHODS: The gram-positive Actinomyces naeslundii (AN, KCTC 5525) and Lactobacillus casei (LC, KCTC 3109) and gram negative Prevotella intermedia (PI, KCTC 3692) that are known to emit red fluorescence were used in this study. Each bacterium was activated in broth and cultivated in different agar media at 37℃ for 7 days. Tryptic soy agar with hemin and vitamin K3 (TSA), TSA with sheep blood (TSAB), basal medium mucin (BMM) medium, and BMM with sheep blood (BMMB) were used in this study. Fluorescence due to bacterial growth was observed under 405-nm wavelength blue light using the quantitative light-induced fluorescence-digital (QLF-D) device. The red, green, and blue fluorescence values of colonies were obtained using image-analysis software and the red to green ratio (R/G value) and red to total RGB ratio (R/RGB value) were calculated for quantitative comparison. RESULTS: The QLF-D images of the AN, LC, and PI colonies showed red fluorescence in all media, but the fluorescence of all bacteria was reduced in TSA and BMM media, compared with in TSAB and BMMB media. Both the R/G and the R/RGB values of all bacteria were significantly reduced in growth media without blood (P<0.001). CONCLUSIONS: Based on this in vitro study, it can be concluded that red fluorescence of oral bacteria can be affected by growth components, especially blood. Blood-containing medium could be a significant factor influencing red fluorescence of oral bacteria. It can be further hypothesized that bleeding in the oral cavity can increase the red fluorescence of dental plaque.