The journal of contemporary dental practice · 2025
Artificial Intelligence-based Caries Risk Prediction Model for Preschool Children.
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Kapoor Deepika, Garg Deepanshu, Jayakumar Pavithraa, Arora Chetna, Dubey Alok, Dash Kailash C, Shetty Prajwal, Mujoo Sheetal, Naik Nischitha
Аннотация
AIMS: To develop and evaluate machine learning models for predicting new caries development in children using clinical and behavioral variables, and to identify the most influential predictors contributing to caries risk.
MATERIALS AND METHODOLOGY: A dataset of 200 preschool children was generated, including variables such as age, sugar intake frequency, baseline dmft, fluoride exposure, mutans streptococci levels, socioeconomic status, oral hygiene, and previous caries. Logistic Regression and Random Forest models were trained and tested using an 80:20 split. Model performance was assessed using accuracy, sensitivity, specificity, precision, and a forest plot of feature importance. Descriptive statistics, group comparisons, and confusion matrices were produced.
RESULTS: Children who developed new caries at 12 months had higher baseline dmft (1.26 vs 0.93), higher sugar intake (2.62 vs 2.00 times/day), and a greater frequency of high mutans levels (61 vs 38%). Random Forest outperformed Logistic Regression with higher accuracy (0.847 vs 0.783) and better sensitivity and specificity. Feature-importance analysis identified Mutans-high, sugar frequency, previous caries, and baseline dmft as the top predictors. The confusion matrix for the Random Forest showed strong predictive reliability [True negatives (TN) = 50, True positives (TP) = 9, False positives (FP) = 4, False negatives (FN) = 5].
CONCLUSION: Machine learning demonstrates strong potential for early prediction of dental caries in children. The Random Forest model showed superior discriminatory performance and identified key risk factors that align with established clinical evidence.
CLINICAL SIGNIFICANCE: Traditional caries risk assessment tools have limited predictive accuracy in preschool children. Artificial Intelligence-based Machine learning offers enhanced predictive capabilities and helps to identify early prediction of caries risk for caries prevention and management. How to cite this article: Kapoor D, Garg D, Jayakumar P, et al. Artificial Intelligence-based Caries Risk Prediction Model for Preschool Children. J Contemp Dent Pract 2025;26(12):1173-1177.
MATERIALS AND METHODOLOGY: A dataset of 200 preschool children was generated, including variables such as age, sugar intake frequency, baseline dmft, fluoride exposure, mutans streptococci levels, socioeconomic status, oral hygiene, and previous caries. Logistic Regression and Random Forest models were trained and tested using an 80:20 split. Model performance was assessed using accuracy, sensitivity, specificity, precision, and a forest plot of feature importance. Descriptive statistics, group comparisons, and confusion matrices were produced.
RESULTS: Children who developed new caries at 12 months had higher baseline dmft (1.26 vs 0.93), higher sugar intake (2.62 vs 2.00 times/day), and a greater frequency of high mutans levels (61 vs 38%). Random Forest outperformed Logistic Regression with higher accuracy (0.847 vs 0.783) and better sensitivity and specificity. Feature-importance analysis identified Mutans-high, sugar frequency, previous caries, and baseline dmft as the top predictors. The confusion matrix for the Random Forest showed strong predictive reliability [True negatives (TN) = 50, True positives (TP) = 9, False positives (FP) = 4, False negatives (FN) = 5].
CONCLUSION: Machine learning demonstrates strong potential for early prediction of dental caries in children. The Random Forest model showed superior discriminatory performance and identified key risk factors that align with established clinical evidence.
CLINICAL SIGNIFICANCE: Traditional caries risk assessment tools have limited predictive accuracy in preschool children. Artificial Intelligence-based Machine learning offers enhanced predictive capabilities and helps to identify early prediction of caries risk for caries prevention and management. How to cite this article: Kapoor D, Garg D, Jayakumar P, et al. Artificial Intelligence-based Caries Risk Prediction Model for Preschool Children. J Contemp Dent Pract 2025;26(12):1173-1177.