Enhancing Skeletal Age Estimation Accuracy Using Support Vector Regression Models

Abstract

ObjectiveThe objective of the study was to determine if support vector regression (SVR) models could enhance the accuracy of skeletal age estimation compared to original metrics. MethodThe study used a dataset of 5,018 individuals from Wuhan, spanning ages 1 to 17. Optimal model parameters were found using cross-validation and grid search techniques. The study compared SVR-based bone age assessment metrics with original metrics and evaluated the performance of the SVR model across different sample sizes. ResultsThe findings unequivocally demonstrated SVR's superior reliability over original metrics in assessing bone age among children in central China. Notably, the SVR-based metric using the combined TW3, CHN05 and GP yields the second highest accuracy, while the SVR-based metric using the combined TW3 and CHN05 yields the highest accuracy. ConclusionThis research highlights SVR's potential for accuracy improvement and robustness with limited datasets.