Prediction of Skeletal Age Through Cervical Vertebral Measurements Using Different Machine Learning Regression Methods.

Abstract

Objective: To compare skeletal ages determined using three different regression methods from measurements made on cervical vertebrae from lateral cephalometric radiographs (LCRs) with the skeletal age determined from hand-wrist radiographs (HWRs).

Methods: LCRs and HWRs of 794 individuals (329 boys, 465 girls) aged 7-18 years were examined. The hand-wrist skeletal age of the participants was determined using the Greulich-Pyle (GP) atlas. Forty-four linear and nine angular morphometric measurements in the C2-C5 vertebrae were made in LCRs. Vertebral skeletal age (VSA) was determined in both sexes using Ridge, the least absolute shrinkage and selection operator (LASSO), and ElasticNet regression methods. The study results were evaluated using R2 (explainability power). Bland-Altman analysis was performed to determine the consistency of chronologic age (CA), GP age, and VSAs.

Results: LASSO regression showed the highest explainability power for VSA, with boys at 0.783 and girls at 0.741. In both sexes, the vertebral depth of concavities had high beta coefficients, and the posterior height of C3 vertebrae (TVup-TVlp) had the highest beta coefficient in boys in LASSO regression. The width of the limits of agreement in both CA and VSA graphs of GP age was wider in boys than in girls. The width of the limits of agreement of CA-VSAs was wider in girls than in boys.

Conclusion: Although high R2 values were obtained, VSA showed no superiority over CA in the assessment of skeletal age, and no significant clinical advantage was observed. For the Turkish population, using GP age may be more accurate for determining skeletal age in orthodontic treatment planning.