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Age Estimation by Pulp/Tooth Ratio in Canines by Mesial and Vestibular Peri‐Apical X‐Rays
Author(s) -
Cameriere Roberto,
Ferrante Luigi,
Belcastro Maria Giovanna,
Bonfiglioli Benedetta,
Rastelli Elisa,
Cingolani Mariano
Publication year - 2007
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/j.1556-4029.2007.00530.x
Subject(s) - dentistry , apposition , orthodontics , molar , pulp (tooth) , medicine , age groups , mathematics , anatomy , demography , sociology
Changes in the size of the pulp canal, caused by apposition of secondary dentine, are the best morphometric parameters for estimating age by X‐rays. The apposition of secondary dentine is the most frequently used method for age estimation in adult subjects. In two previous papers, we studied the application of the pulp/tooth area ratio by peri‐apical X‐rays as an indicator of age at death. The aim of the present study was to test the accuracy of age evaluation by combined analysis of labio‐lingual and mesial peri‐apical X‐rays of lower and upper canines. A total of 200 such X‐rays were assembled from 57 male and 43 female skeletons of Caucasian origin, aged between 20 and 79 years. For each skeleton, dental maturity was evaluated by measuring the pulp/tooth area ratio according to labio‐lingual and mesial X‐rays on upper ( x 1 , x 2 ) and lower ( x 3 , x 4 ) canines. Very good agreement was found between intra‐observer measurements. Statistical analysis showed that all variables x 1 , x 2 , x 3 , and x 4 and the first‐order interaction between x 1 and x 3 contributed significantly to the fit, so that they were included in the regression model, yielding the following regression formula:The residual standard error of estimated ages was 3.62 years, with 94 degrees of freedom, and the median of the residuals was −0.155 years, with an interquartile range of 4.96 years. The accuracy of the method was ME = 2.8 years, where ME is the mean prediction error. The model also explained 94% of total variance ( R 2 = 0.94).