1 |
Liu et al. [12] |
2022 |
Cross-sectional |
CNNs |
Develop a semi-automatic model to detect ectopic eruption of maxillary first molars in 4–9-year-olds’ radiographs |
High sensitivity and specificity in automated screening |
The algorithm may enhance clinical diagnosis and management of ectopic eruption |
2 |
Wu et al. [16] |
2021 |
Cross-sectional |
ANNs |
Create an ML model to identify caries-related oral microbes in mother–child dyads |
Desirable results for both mothers and children |
Further refinement needed by considering more variables |
3 |
Park et al. [13] |
2021 |
Cross-sectional |
ANNs |
Predict early childhood caries using ML-based AI models (XGBoost, random forest, and Light GBM algorithms) |
Favorable performance in dental caries prediction with satisfactory AUC values |
Helpful in identifying high-risk groups and applying preventive measures |
4 |
Pang et al. [19] |
2021 |
Cross-sectional |
ANNs |
Develop a caries risk prediction model for teenagers by considering environmental and genetic factors |
Accurate identification of individuals at high and very high risk of developing caries |
Potential as a powerful tool for performing community-level high caries risk identification |
5 |
Karhade et al. [14] |
2021 |
Cross-sectional |
ANNs |
Evaluate the accuracy of an automated ML algorithm for early childhood caries (ECC) classification |
Comparable performance to that of the reference model (AUC: 0.74, sensitivity: 0.67, PPV: 0.64) |
Valuable tool for ECC screening |
6 |
Ramos-Gomez et al. [15] |
2021 |
Cross-sectional |
ANNs |
Identify survey items to predict dental caries in children using a machine learning algorithm |
Algorithm toolkits can help dental professionals to assess children’s oral health |
Demonstrates potential for dental caries screening in children |