ArticleDentistry journal2021
Using a Machine Learning Algorithm to Predict the Likelihood of Presence of Dental Caries among Children Aged 2 to 7.
Article in Dentistry journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review.Clinical and experimental dental research · 2024Pooled it
- From Clinic to Community: An Interpretable Artificial Intelligence Framework for Enamel Caries Detection to Support Public Health Dentistry.European journal of dentistry · 2026Article
- Will Artificial Intelligence and Digital Impressions Erode the Art of Clinical Intuition?International journal of paediatric dentistry · 2026Article
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
- Metabolic profiling of severe early childhood caries and black stain: pathway and biomarker discovery in 3-6-year-olds.Frontiers in molecular biosciences · 2026Article
- Mathematical and AI-Based Predictive Modelling for Dental Caries Risk Using Clinical and Behavioural Parameters.Bioengineering (Basel, Switzerland) · 2025Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Early childhood caries risk prediction using machine learning approaches in Bangladesh.BMC oral health · 2025Article
- Tech Bytes-Harnessing Artificial Intelligence for Pediatric Oral Health: A Scoping Review.International journal of clinical pediatric dentistry · 2024Article
- The Use of Artificial Intelligence in Caries Detection: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- Review
- The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature.Healthcare (Basel, Switzerland) · 2024Article
- Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making.Diagnostics (Basel, Switzerland) · 2024Review
- Utilization of machine learning models in predicting caries risk groups and oral health-related risk factors in adults.BMC oral health · 2024Article
- An overview of artificial intelligence based automated diagnosis in paediatric dentistry.Frontiers in oral health · 2024Review
- Trends of machine learning for dental caries research in Southeast Asia: insights from a bibliometric analysis.F1000Research · 2024Article
- Influence of Artificial Intelligence-Driven Diagnostic Tools on Treatment Decision-Making in Early Childhood Caries: A Systematic Review of Accuracy and Clinical Outcomes.Dentistry journal · 2023Review
- Artificial Intelligence Its Uses and Application in Pediatric Dentistry: A Review.Biomedicines · 2023Review
- Optimal Feature Selection-Based Dental Caries Prediction Model Using Machine Learning for Decision Support System.Bioengineering (Basel, Switzerland) · 2023Article
- The Link between Stroke Risk and Orodental Status-A Comprehensive Review.Journal of clinical medicine · 2022Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundDental 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.
methodsThe 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.
resultsSurvey 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).
conclusionOur findings demonstrate the potential of screening for active caries and caries experience among children using surveys answered by their parents.
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