Han Du1, Gang Li2, Qiang Zheng3, Jie Yang4. 1. Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, #22 Zhongguancun Nandajie, Hai Dian District, Beijing, 100081, China. 2. Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, #22 Zhongguancun Nandajie, Hai Dian District, Beijing, 100081, China. kqgang@bjmu.edu.cn. 3. School of Computer and Control Engineering, Yantai University, Yantai, China. 4. Division of Oral and Maxillofacial Radiology, Temple University Kornberg School of Dentistry, Philadelphia, PA, USA.
Abstract
OBJECTIVES: To validate the fitness of the age estimation model in Black Americans, which was previously and solely established for the Chinese population based on pulp chamber volume of the first molars from cone beam computed tomography (CBCT), and to establish a new age estimation model for Black Americans. MATERIALS AND METHODS: A total of 203 subjects with CBCT scans, including 119 Chinese and 84 Black Americans, were retrospectively identified. The age range of subjects was between 11 and 87 years. For both populations, automated 3D pulp chamber segmentation of the first molars was performed by deep learning, followed by volume calculation and age estimation by a logarithmic regression model, which was established in a prior study solely on Chinese population. Additionally, a separate logarithmic regression analysis was carried out on Black Americans. The performance of age estimation was assessed by the mean absolute error (MAE), root mean square error (RMSE), Wilcoxon signed rank test, and coefficient of determination (R2) between the actual and estimated human ages. RESULTS: When applying the age estimation model established in the prior study, MAE = 7.994 years and RMSE = 10.065 years were observed in the Chinese population, while MAE = 14.049 years and RMSE = 17.866 years were observed in Black Americans. The new age estimation model established for Black Americans was AGE = 89.752 - 21.176 × lnV (V = pulp chamber volume), with MAE = 7.930 years, RMSE = 10.664 years, and coefficient of determination (R2) = 0.600. CONCLUSIONS: Population-specific age estimation is needed when applied in Black Americans and Chinese people based on pulp chamber volume of the first molars from CBCT.
OBJECTIVES: To validate the fitness of the age estimation model in Black Americans, which was previously and solely established for the Chinese population based on pulp chamber volume of the first molars from cone beam computed tomography (CBCT), and to establish a new age estimation model for Black Americans. MATERIALS AND METHODS: A total of 203 subjects with CBCT scans, including 119 Chinese and 84 Black Americans, were retrospectively identified. The age range of subjects was between 11 and 87 years. For both populations, automated 3D pulp chamber segmentation of the first molars was performed by deep learning, followed by volume calculation and age estimation by a logarithmic regression model, which was established in a prior study solely on Chinese population. Additionally, a separate logarithmic regression analysis was carried out on Black Americans. The performance of age estimation was assessed by the mean absolute error (MAE), root mean square error (RMSE), Wilcoxon signed rank test, and coefficient of determination (R2) between the actual and estimated human ages. RESULTS: When applying the age estimation model established in the prior study, MAE = 7.994 years and RMSE = 10.065 years were observed in the Chinese population, while MAE = 14.049 years and RMSE = 17.866 years were observed in Black Americans. The new age estimation model established for Black Americans was AGE = 89.752 - 21.176 × lnV (V = pulp chamber volume), with MAE = 7.930 years, RMSE = 10.664 years, and coefficient of determination (R2) = 0.600. CONCLUSIONS: Population-specific age estimation is needed when applied in Black Americans and Chinese people based on pulp chamber volume of the first molars from CBCT.
Authors: Sudheer B Balla; Srikanth Aryasri Ankisetti; Anjum Bushra; Vimal Bharathi Bolloju; Ali Mir Mujahed; Alekhya Kanaparthi; Sai Shravani Buddhavarapu Journal: Int J Legal Med Date: 2020-01-25 Impact factor: 2.686
Authors: R Cameriere; E Cunha; S N Wasterlain; S De Luca; E Sassaroli; F Pagliara; E Nuzzolese; M Cingolani; L Ferrante Journal: J Forensic Leg Med Date: 2013-04-06 Impact factor: 1.614