# Machine Learning for Child Oral Health: A Scoping Review
## 3 Results
### Global Distribution and Economic Regions  
Geographically, the Americas contributed the highest number of studies (51.7%), followed by Asia (37.9%), with Europe making the least contribution (10.3%) (Figure 2). HICs dominated the literature (69.0%), particularly in categories of ML models such as prediction (60.0%) and diagnostics (40.0%). UMICs contributed 27.6%, mainly focusing on prediction (75.0%) and diagnostics (25.0%). LMICs made the smallest contribution (3.4%), solely in the domain of prediction (Figures 2 and 3).  
Figure 2: Global distribution of publications on child oral health using machine learning, categorized by country income levels. High-income countries (dark gold) such as the USA, Canada, and parts of Europe demonstrate the highest research output. Upper-middle-income countries (medium gold), including China and Brazil, show moderate contributions, while lower-middle-income countries (light gold), such as Iran, reflect emerging research activities. The map highlights disparities in research productivity based on income levels, with a compass rose and scale bar for spatial reference.  
Table 5 highlights the variations in ML applications by economic region. HICs exhibited the most diverse range of ML models and applications, with prediction and diagnostics dominating dental specialties such as dental public health, pediatric dentistry, and orthodontics. For example, random forest, regression, neural network, and gradient boosting classifiers were commonly employed across specialties, with deep learning prominently used for orthodontic diagnosis. In contrast, UMICs focused heavily on random forests and neural networks for prediction and diagnostic tasks in pediatric and general dentistry. LMICs had the narrowest scope, primarily utilizing neural networks, decision trees, and gradient boosting classifiers for predictive tasks in dental public health.  
Figure 3: Trends in the use of machine learning (ML) for diagnosis, management, and prediction in dentistry across income groups (HIC: high-income countries, UMIC: upper-middle-income countries, LMIC: lower-middle-income countries) from 2001–2023. The size of the circles represents the number of studies, while the colors indicate the type of application: red for diagnosis and orange for prediction. Notably, high-income countries show a greater and consistent use of ML for diagnosis and prediction, particularly in recent years (2021–2023). Upper- and lower-middle-income countries exhibit increasing but less consistent adoption, with a focus on prediction in LMICs. This highlights disparities in the adoption of ML technologies across economic regions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Economic Region** & **Specialty** & **Purpose** & **Common ML Models** \\ \hline \multirow{6}{*}{HIC} & \multirow{2}{*}{Dental public health} & \multirow{2}{*}{Prediction, diagnosis} & Random forest, gradient boosting classifiers, neural network, deep learning, regression \\  & & & Random forest, neural network, Regression, \\  & & & Gradient boosting classifiers \\  & Orthodontist & Diagnosis & deep learning \\  & General & Diagnosis, prediction & Clustering method, neural network \\  & Forensic & Diagnosis & clustering method \\ \hline \multirow{6}{*}{UMIC} & \multirow{2}{*}{Dental public health} & \multirow{2}{*}{Prediction, Random forest} \\  & & & Random forest \\ \cline{1-1}  & & & neural networks, regression \\ \cline{1-1}  & & & deep learning \\ \hline \multirow{2}{*}{LMIC} & \multirow{2}{*}{Dental public health} & \multirow{2}{*}{Prediction, Neural networks, gradient boosting classifiers, decision tree, random forest, support vector machine} \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table}
Table 5: Application of machine learning models in dental specialties across economic regions.