Age Estimation by Machine Learning and CT-Multiplanar Reformation of Cranial Sutures in Northern Chinese Han Adults.

Objectives: To establish age estimation models of northern Chinese Han adults using cranial suture images obtained by CT and multiplanar reformation (MPR), and to explore the applicability of cranial suture closure rule in age estimation of northern Chinese Han population.

Methods: The head CT samples of 132 northern Chinese Han adults aged 29-80 years were retrospectively collected. Volume reconstruction (VR) and MPR were performed on the skull, and 160 cranial suture tomography images were generated for each sample. Then the MPR images of cranial sutures were scored according to the closure grading criteria, and the mean closure grades of sagittal suture, coronal sutures (both left and right) and lambdoid sutures (both left and right) were calculated respectively. Finally taking the above grades as independent variables, the linear regression model and four machine learning models for age estimation (gradient boosting regression, support vector regression, decision tree regression and Bayesian ridge regression) were established for northern Chinese Han adults age estimation. The accuracy of each model was evaluated.

Results: Each cranial suture closure grade was positively correlated with age and the correlation of sagittal suture was the highest. All four machine learning models had higher age estimation accuracy than linear regression model. The support vector regression model had the highest accuracy among the machine learning models with a mean absolute error of 9.542 years.

Conclusions: The combination of skull CT-MPR and machine learning model can be used for age estimation in northern Chinese Han adults, but it is still necessary to combine with other adult age estimation indicators in forensic practice.