RESUMEN
Objective To explore the feasibility of the CT image reconstruction of laryngeal cartilage and hyoid bone in adult age estimation using data mining methods. Methods The neck thin slice CT scans of 413 individuals aged 18 to <80 years were collected and divided into test set and train set, randomly. According to grading methods such as TURK et al., all samples were graded comprehensively. The process of thyroid cartilage ossification was divided into 6 stages, the process of cricoid cartilage ossification was divided into 5 stages, and the synosteosis between the greater horn of hyoid and hyoid body was divided into 3 stages. Multiple linear regression model, support vector regression model, and Bayesian ridge regression model were developed for adult age estimation by scikit-learn 0.17 machine learning kit （Python language）. Leave-one-out cross-validation and the test set were used to further evaluate performance of the models. Results All indicators were moderately or poorly associated with age. The model with the highest accuracy in male age estimation was the support vector regression model, with a mean absolute error of 8.67 years, much higher than the other two models. The model with the highest accuracy in female adult age estimation was the support vector regression model, with a mean absolute error of 12.69 years, but its accuracy differences with the other two models had no statistical significance. Conclusion Data mining technology can improve the accuracy of adult age estimation, but the accuracy of adult age estimation based on laryngeal cartilage and hyoid bone is still not satisfactory, so it should be combined with other indicators in practice.