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Objective The Kellinghaus grading method was used to manually read and grade the thin-layer CT of sternal end of clavicle,and a variety of traditional statistical methods as well as machine learning methods were used to construct age estimation models for adolescents and adults in early adulthood,to explore the value of the application of machine learning technology in the study of age estimation of the Han Chinese population in Sichuan.Methods Thin-section CT images of the chest were retrospectively collected from 491 individuals aged 10～30 years,and the collected samples were assigned a reading grade with reference to the Kellinghaus grading method.10%of the xases were randomly selected as the test set,and the remaining data were used as the training set to construct a variety of traditional statistical regression models and machine learning models for estimating the age of adolescents and adults in early adulthood,and the performance of the models was evaluated by using the mean absolute error(MAE).Results The statistical regression model with the best efficacy was the cubic regression model,with an MAE value of 1.34 for males and 1.57 for females;of the three machine learning models,the Random Forest model had the best predictive efficacy for males,with an MAE value of 1.39,and the Support Vector model had the best predictive efficacy for females,with an MAE value of 1.51.Conclusion In the construction of age estimation models for sternal end of clavicle,the machine learning model has a certain improvement in the accuracy of age prediction,but there is no obvious advantage compared with the traditional statistical regression model,and the use of the machine learning method in age estimation based on sternal end of clavicle still needs further exploration.

Humans ; Male ; Middle Aged ; Multiple Myeloma ; complications ; diagnosis ; Spondylitis, Ankylosing ; complications