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To establish population-specific age estimation models in adults from costal cartilage for contemporary Chinese by using three-dimensional volume-rendering technique. Five hundred and twelve individuals (254 females and 258 males) with documented ages between 20 and 85 years were retrospectively included. Their clinical CT examinations (1 mm slice thickness) were used to develop the sex-specific age prediction model. A validation sample comprising 26 female and 24 male individuals was then used to test the predictive accuracy of the established models. Simple linear regression (SLR), multiple linear regression (MLR), gradient boosting regression (GBR), support vector machine (SVM), and decision tree regression (DTR) were utilized to build the age diagnosis models from calibration samples. By comparison, the decision tree regression was the relatively more accurate age prediction model for male, with mean absolute error = 5.31 years, least absolute error = 0.10 years, correct percentage within 5 years = 54%, and the correct percentage within 10 years = 88%. The stepwise multiple linear regression equations was the relatively more accurate one for female, with mean absolute error = 6.72 years, least absolute error = 0.68 years, correct percentage within 5 years = 42%, and correct percentage within 10 years = 77%. Our results indicated that the present established age estimation model can be applied as an additional guidance for age estimation in adults.

Objectives: This study aimed to explore whether computed tomography (CT) images of cranial sutures can contribute to adult age estimation in Chinese Han individuals. Materials and methods: This study was based on cranial CT scans of 230 ChineseHan males aged 23.33-76.93 years. A total of 160 images from 16 suture segments were scored after volume reformation and multiplanar reconstruction in each individual. Decision tree regression, linear support vector regression, Bayesian ridge regression, and gradient boosting regression were developed for adult age estimation by a training set using leave-one-out cross-validation and further evaluated by the test set. The inaccuracy and bias were calculated to evaluate the four models and the previously used models from the literature. Results:The degree of suture closure was associated with adult age. The minimum inaccuracy of the test set was 7.73 years obtained by linear support vector regression, while the inaccuracy of previous simple linear regression models was 13.09 and 10.97 years. The accuracy was higher in the age group from 40.00 to 59.99 years compared to the other age groups.Discussion: The accuracy of our models for adult age estimation was superior to those in previous studies based on cranial sutures. Hence, the application of novel statistical data mining tools helps to improve aging issues. Nevertheless, age estimation of adults should be combined with other methods, since the accuracy level is still not satisfactory. K E Y W O R D Scranial sutures, data mining, forensic anthropology, multidetector computed tomography, skeletal age determination

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