Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7

Abstract

Background: Dental caries is the most common chronic childhood infectious disease and is a serious public health problem affecting both developing and industrialized countries, yet it is preventable in most cases. This study evaluated the potential of screening for dental caries among children using a machine learning algorithm applied to parent perceptions of their child’s oral health assessed by survey. Methods: The sample consisted of 182 parents/caregivers and their children 2–7 years of age living in Los Angeles County. Random forest (a machine learning algorithm) was used to identify survey items that were predictors of active caries and caries experience. We applied a three-fold cross-validation method. A threshold was determined by maximizing the sum of sensitivity and specificity conditional on the sensitivity of at least 70%. The importance of survey items to classifying active caries and caries experience was measured using mean decreased Gini (MDG) and mean decreased accuracy (MDA) coefficients. Results: Survey items that were strong predictors of active caries included parent’s age (MDG = 0.84; MDA = 1.97), unmet needs (MDG = 0.71; MDA = 2.06) and the child being African American (MDG = 0.38; MDA = 1.92). Survey items that were strong predictors of caries experience included parent’s age (MDG = 2.97; MDA = 4.74), child had an oral health problem in the past 12 months (MDG = 2.20; MDA = 4.04) and child had a tooth that hurt (MDG = 1.65; MDA = 3.84). Conclusion: Our findings demonstrate the potential of screening for active caries and caries experience among children using surveys answered by their parents.

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 ].…”

Optimal Feature Selection based Dental Caries Prediction Model Using Machine Learning for Decision Support System

“…As a result of the study, the model performance of single-item reporting was the highest, with AUROC 0.74, Se 0.67, and PPV 0.64. In a similar case study, Ramos-Gomez et al [23] identifies variables for induced dental caries in infants 2-7 years of age living in Los Angeles. A random forest algorithm is trained to identify dental caries predictors.…”

The Link between Stroke Risk and Orodental Status—A Comprehensive Review

“…Dental infections brought on by poor oral hygiene are among the most common chronic bacterial illnesses, frequently affecting those who avoid or put off going to the dentist. Gingivitis, periodontitis, and dental caries are among the most prevalent chronic orodental diseases in humans [ 6 , 7 ]. Dental infections can spread to nearby bone and soft tissues after starting inside a tooth or one of its supporting components.…”