The role of multislice computed tomography of the costal cartilage in adult age estimation.
Int J Legal Med
; 132(3): 791-798, 2018 May.
Article
em En
| MEDLINE
| ID: mdl-28717963
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Determinação da Idade pelo Esqueleto
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Tomografia Computadorizada Multidetectores
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Cartilagem Costal
Tipo de estudo:
Guideline
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Observational_studies
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Prognostic_studies
Limite:
Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Int J Legal Med
Assunto da revista:
JURISPRUDENCIA
Ano de publicação:
2018
Tipo de documento:
Article
País de publicação:
Alemanha