Demirjian法结合机器学习算法推断北方汉族儿童及青少年牙龄OA北大核心CHSSCDCSTPCDMEDLINE

Objective To investigate the application value of combining the Demirjian's method with ma-chine learning algorithms for dental age estimation in northern Chinese Han children and adolescents.Methods Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected.The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method.Various machine learning algorithms,including support vector re-gression(SVR),gradient boosting regression(GBR),linear regression(LR),random forest regression(RFR),and decision tree regression(DTR)were employed.Age estimation models were constructed based on total,female,and male samples respectively using these algorithms.The fitting performance of different machine learning algorithms in these three groups was evaluated.Results SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples,while GBR showed the best performance in male samples.The mean absolute error(MAE)of the op-timal age estimation model was 1.246 3,1.281 8 and 1.153 8 years in the total,female and male samples,respectively.The optimal age estimation model exhibited varying levels of accuracy across dif-ferent age ranges,which provided relatively accurate age estimations in individuals under 18 years old.Conclusion The machine learning model developed in this study exhibits good age estimation effi-ciency in northern Chinese Han children and adolescents.However,its performance is not ideal when applied to adult population.To improve the accuracy in age estimation,the other variables can be con-sidered.