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Validation of Machine Learning Models for Craniofacial Growth Prediction
Eungyeong Kim1, Yasuhiro Kuroda2, Yoshiki Soeda2
1Department of Orthodontics, School of Dentistry, Kanagawa Dental University, Yokosuka 238-8580, Japan.
Insights
The least absolute shrinkage and selection operator (LASSO) model accurately predicts craniofacial growth in Japanese children. This machine learning approach aids in forecasting individual skeletal development for orthodontic treatment planning.
Area of Science:
- Orthodontics
- Craniofacial Biology
- Biomedical Engineering
Background:
- Predicting longitudinal craniofacial growth is crucial for effective orthodontic treatment planning.
- Accurate growth prediction aids in personalized treatment strategies and improved outcomes.
- Previous models have limitations in predicting individual growth trajectories.
Purpose of the Study:
- To identify the most accurate predictive model for longitudinal craniofacial growth in a Japanese pediatric population.
- To compare the performance of various statistical and machine learning models in growth prediction.
- To evaluate the utility of the best-performing model for future orthodontic applications.
Main Methods:
- Longitudinal lateral cephalometric radiographs from 59 Japanese children (ages 6-12) were analyzed.
- Models included multiple regression, least absolute shrinkage and selection operator (LASSO), radial basis function network, multilayer perceptron, and gradient-boosted decision tree.
- Independent variables comprised skeletal landmarks, linear, and angular parameters; dependent variables were future cephalometric values at age 13.
- Root-mean-square error was used to assess prediction accuracy.
Main Results:
- The LASSO model demonstrated the smallest average error across skeletal landmarks, linear, and angular parameters.
- Highest prediction accuracies achieved were 97.87% for skeletal linear parameters and 94.45% for skeletal angular parameters at age 13.
- The LASSO model effectively integrated multiple independent variables for robust prediction.
Conclusions:
- The LASSO model is the most accurate for predicting longitudinal craniofacial growth in the studied Japanese population.
- This predictive capability is highly valuable for personalized orthodontic treatment planning.
- The model's accuracy supports its application in anticipating individual growth patterns for clinical use.
Abstract:
This study identified the most accurate model for predicting longitudinal craniofacial growth in a Japanese population using statistical methods and machine learning. Longitudinal lateral cephalometric radiographs were collected from 59 children (27 boys and 32 girls) with no history of orthodontic treatment. Multiple regression analysis, least absolute shrinkage and selection operator, radial basis function network, multilayer perceptron, and gradient-boosted decision tree were used. The independent variables included 26 coordinated values of skeletal landmarks, 13 linear skeletal parameters, and 17 angular skeletal parameters in children ages 6 to 12 years. The dependent variables were the values of the 26 coordinated skeletal landmarks, 13 skeletal linear parameters, and 17 skeletal angular parameters at 13 years of age. The difference between the predicted and actual measured values was calculated using the root-mean-square error. The prediction model for craniofacial growth using the least absolute shrinkage and selection operator had the smallest average error for all values of skeletal landmarks, linear parameters, and angular parameters. The highest prediction accuracies when predicting skeletal linear and angular parameters for 13-year-olds were 97.87% and 94.45%, respectively. This model incorporates several independent variables and is useful for future orthodontic treatment because it can predict individual growth.
