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International Journal of Legal Medicine
Published in: International Journal of Legal Medicine 6/2019

01-11-2019 | Computed Tomography | Original Article

Age estimation using pulp/enamel volume ratio of impacted mandibular third molars measured on CBCT images in a northern Chinese population

Authors: Zhi-yong Zhang, Chun-xia Yan, Qiao-mei Min, Shao-qing Li, Jing-si Yang, Yu-cheng Guo, Wen-fan Jin, Lan-jiang Li, Pan-Fen Xing, Jun Li

Published in: International Journal of Legal Medicine | Issue 6/2019

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Abstract

The present study aims to evaluate the relation between chronological age and the ratio of pulp volume (PV) to enamel volume (EV) of impacted mandibular third molars (IMTMs) by using cone-beam computed tomography (CBCT) images and an improved 3D image segmentation technique. A sample of CBCT images of IMTM was collected from 414 northern Chinese subjects (214 male and 200 female clinical patients) ranging in age from 20 to 65 years. The GrowCut effect image segmentation (GCEIS) module algorithm was used to calculate the PV and EV from CBCT images. The total sample was divided into a training group and validation group in a ratio of 7 to 3. The PV/EV ratio (PEr) in the training sample was used to develop a mathematical formula for age estimation as follows: age = − 5.817–21.726 × Ln PEr (p < 0.0001) (Ln, natural logarithm). The mean absolute error (MAE) and root mean square error (RMSE) were used to determine the precision and accuracy of the mathematical formula in the validation group and all samples. The MAEs in the male, female, and pooled gender samples were 9.223, 7.722, and 8.41, respectively, and the RMSEs in the male, female, and pooled gender samples were 10.76, 9.58, and 9.986, respectively. The precise and accurate results indicate that the PEr of IMTM in CBCT images is a potential index for dental age estimation and is possible to be used in forensic medicine.
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Metadata
Title
Age estimation using pulp/enamel volume ratio of impacted mandibular third molars measured on CBCT images in a northern Chinese population
Authors
Zhi-yong Zhang
Chun-xia Yan
Qiao-mei Min
Shao-qing Li
Jing-si Yang
Yu-cheng Guo
Wen-fan Jin
Lan-jiang Li
Pan-Fen Xing
Jun Li
Publication date
01-11-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Legal Medicine / Issue 6/2019
Print ISSN: 0937-9827
Electronic ISSN: 1437-1596
DOI
https://doi.org/10.1007/s00414-019-02112-2

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