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Erschienen in:

17.07.2017 | Original Article

The role of multislice computed tomography of the costal cartilage in adult age estimation

verfasst von: Kui Zhang, Fei Fan, Meng Tu, Jing-hui Cui, Jing-song Li, Zhao Peng, Zhen-hua Deng

Erschienen in: International Journal of Legal Medicine | Ausgabe 3/2018

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Abstract

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.
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Metadaten
Titel
The role of multislice computed tomography of the costal cartilage in adult age estimation
verfasst von
Kui Zhang
Fei Fan
Meng Tu
Jing-hui Cui
Jing-song Li
Zhao Peng
Zhen-hua Deng
Publikationsdatum
17.07.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Legal Medicine / Ausgabe 3/2018
Print ISSN: 0937-9827
Elektronische ISSN: 1437-1596
DOI
https://doi.org/10.1007/s00414-017-1646-y

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