A New Model for Caries Risk Prediction in Teenagers Using a Machine Learning Algorithm Based on Environmental and Genetic Factors

Abstract

Dental caries is a multifactorial disease that can be caused by interactions between genetic and environmental risk factors. Despite the availability of caries risk assessment tools, caries risk prediction models incorporating new factors, such as human genetic markers, have not yet been reported. The aim of this study was to construct a new model for caries risk prediction in teenagers, based on environmental and genetic factors, using a machine learning algorithm. We performed a prospective longitudinal study of 1,055 teenagers (710 teenagers for cohort 1 and 345 teenagers for cohort 2) aged 13 years, of whom 953 (633 teenagers for cohort 1 and 320 teenagers for cohort 2) were followed for 21 months. All participants completed an oral health questionnaire, an oral examination, biological (salivary and cariostate) tests, and single nucleotide polymorphism sequencing analysis. We constructed a caries risk prediction model based on these data using a random forest with an AUC of 0.78 in cohort 1 (training cohort). We further verified the discrimination and calibration abilities of this caries risk prediction model using cohort 2. The AUC of the caries risk prediction model in cohort 2 (testing cohort) was 0.73, indicating high discrimination ability. Risk stratification revealed that our caries risk prediction model could accurately identify individuals at high and very high caries risk but underestimated risks for individuals at low and very low caries risk. Thus, our caries risk prediction model has the potential for use as a powerful community-level tool to identify individuals at high caries risk.

Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review

“…AI models developed for application in pedodontics have mainly focused on: dental plaque on primary teeth ( n = 1) [ 21 ], ECC ( n = 6) [ 25 , 26 , 27 , 28 , 29 , 30 ], fissure sealant categorization ( n = 1) [ 31 ], mesiodens and supernumerary tooth identification ( n = 6) [ 3 , 10 , 22 , 23 , 24 , 41 ], chronological age assessment ( n = 4) [ 32 , 33 , 37 , 38 ], identification of deciduous and young permanent teeth ( n = 3) [ 34 , 35 , 39 ], children’s oral Health ( n = 2) [ 7 , 20 ], and ectopic eruption ( n = 2) [ 39 , 40 ] ( Figure 3 ).…”

“…Articles without complete texts, narrative reviews, scoping reviews, letters to the editor, opinion letters, case reports, brief communications, conference proceedings, and non-English language articles (84 articles) were all omitted ( Figure 1 ). Finally, only 25 papers met the qualifying requirements [ 3 , 7 , 10 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

MMDCP: Multi-Modal Dental Caries Prediction for Decision Support System Using Deep Learning

“…This study yielded an accuracy, sensitivity, specificity, and AUC-ROC values of 77.29%, 85.16%, 70.27% and 0.626, respectively, while our model displayed higher values, as described in the model evaluation result section. The authors of [ 41 ] published a novel model to predict the risk of caries among 1055 teenagers using ML algorithm (random forest (RF) and logistic regression (LR)). While experimenting with the training set and evaluating their models on the test set, they obtained an AUC-ROC of 0.74 for LR and 0.73 for RF.…”

Mobile Application Design: Caries Health Care for Children

“…In dentistry, radiographic information is a challenging process, since the whole process is based on an image; where the pathology must be completely identified. For this reason, a mobile application called" PantoDict" was developed to provide information about panoramic X-rays [20]. For this reason, two groups were evaluated, both of which practiced taking these radiographs with the aid of the application.…”