Elsevier

Forensic Science International

Volume 258, January 2016, Pages 50-54
Forensic Science International

Age estimation in children by measurement of open apices in teeth with Bayesian calibration approach

https://doi.org/10.1016/j.forsciint.2015.11.005Get rights and content

Highlights

  • Age estimation by radiological analysis has wide applications in scientific fields.

  • Bayesian calibration method appears to be suitable for assessing age.

  • Forensic age estimation is essential in resolving a variety of legal questions.

  • Age assessment is useful in diagnosis and in orthodontic treatment.

Abstract

Age estimation from teeth by radiological analysis, in both children and adolescents, has wide applications in several scientific and forensic fields. In 2006, Cameriere et al. proposed a regression method to estimate chronological age in children, according to measurements of open apices of permanent teeth.

Although several regression models are used to analyze the relationship between age and dental development, one serious limitation is the unavoidable bias in age estimation when regression models are used.

The aim of this paper is to develop a full Bayesian calibration method for age estimation in children according to the sum of open apices, S, of the seven left permanent mandibular teeth.

This cross-sectional study included 2630 orthopantomographs (OPGs) from healthy living Italian subjects, aged between 4 and 17 years and with no obvious developmental abnormalities. All radiographs were in digital format and were processed by the ImageJ computer-aided drawing program. The distance between the inner side of the open apex was measured for each tooth. Dental maturity was then evaluated according to the sum of normalized open apices (S).

Intra- and inter-observer agreement was satisfactory, according to an intra-class correlation coefficient of S on 50 randomly selected OPGs. Mean absolute errors were 0.72 years (standard deviation 0.60) and 0.73 years (standard deviation 0.61) in boys and girls, respectively. The mean interquartile range (MIQR) of the calibrating distribution was 1.37 years (standard deviation 0.46) and 1.51 years (standard deviation 0.52) in boys and girls, respectively. Estimate bias was βERR = −0.005 and 0.003 for boys and girls, corresponding to a bias of a few days for all individuals in the sample. Neither of the βERR values was significantly different from 0 (p > 0.682).

In conclusion, the Bayesian calibration method overcomes problems of bias in age estimation when regression models are used, and appears to be suitable for assessing both age and age distribution in children according to tooth maturity.

Introduction

Evaluation of dental age by morphological and radiological analysis, in both children and adolescents, has wide applications in several scientific and forensic fields. For forensic purposes, the problem of age estimation (AE) answers several legal questions in cases as diverse as, for instance, adoption (child's date of birth is unknown or birth certificate is false) [1]; illegal cross-border migration; foreigners are unable to provide documentary evidence of their date of birth, or have no valid identity documents [2], [3]; sexual exploitation; criminology (how close true age is to the minimum age for criminal responsibility in various countries).

As regards clinical purposes, age assessment is useful in diagnosis and treatment planning; it is also an essential question in pediatric endocrinology and orthodontic treatment [4]. Many studies have shown that boys have a delay in skeletal growth with respect to girls, which can also be proved by tooth analysis [5].

Nowadays the most frequent method used for biological AE, especially in children and adolescents, is radiological analysis of some anatomical districts. Several methods have been applied, such as analysis of skeletal development of hand-wrist bones [5], [6], [7], knee, clavicle and teeth [8], [9].

Easy to analyze by X-rays, dental structures are becoming more and more useful in estimating age: they represent a less variable measure than eruption, being unaffected by factors such as malnutrition, premature loss of primary teeth, crowding, or dental decay. Tooth formation also has high heritability, a low coefficient of variation, and is more resistant to environmental effects [8], [9]. However, tooth age estimation becomes particularly difficult when the teeth are fully developed (around 14 years) [5], [8] and, over the age of 14, third molar development may be useful [10].

With X-rays, it became possible to define the mineralization stages of dental structures: in 1973, Demirjian et al. [11] developed a method for dental age assessment, based on evaluation of one of eight appropriate radiographic stages (A–H) of crown and root development on permanent teeth from the left side of the mandible, excluding the third molar. In 2001, Willems et al. [12] reviewed Demirjian's method and created a new scoring system, which became the best adaptation of Demirjian's.

In 2006, Cameriere et al. proposed a method of estimating chronological age in children, with a formula which results from the relationship between age and measurement of open apices of permanent teeth. OPGs were processed by a computer-aided drafting program, so that the ratio of the distance between the inner surfaces of the apices (and their sum in multi-rooted teeth) and the entire length of the tooth could be calculated, to avoid the effects of any differences in magnification and angulation among X-rays. AE has been evaluated with the ratio of the seven permanent left mandibular teeth, the sum of open apices, and the number of teeth with closed apices [13].

The method was first applied to a sample of 455 Italian and Caucasian children, and produced accurate and reliable estimates. After these results, the authors extended the method to samples from Kosovo and Slovenia [14], and in 2007 started to test several samples from other European countries (Croatia, Germany, Spain, UK), thus obtaining a formula which gave different results for different nations [15]. It has recently been reported that Cameriere's method is more accurate than other methods for AE of children in the age group 6–13 years [16], [17], [18], [19], [20]. Although several regression models have been used to analyze the relationship between age and dental development, one serious limitation is the unavoidable bias when regression models are used.

In fact, as noted by Aykroyd et al. [21], linear regression models tend to overestimate age in juveniles and underestimate it in adults, which is exactly what should be avoided in the forensic setting. To overcome this bias, Lucy and Pollard [22] suggested calibration methods.

The aim of this paper is to develop a full Bayesian calibration method for AE in children according to the sum of open apices, S, of the seven left permanent mandibular teeth.

Section snippets

Sample

This cross-sectional study included 2630 orthopantomographs (OPGs) from healthy living Italian subjects, aged between 4 and 17 years and with no obvious developmental abnormalities. Samples were collected from Radmedica (Radiologia Odontoiatrica Digitale) of Rome. Table 1 lists the age and gender distribution of the sample.

All subjects were Italian descendants living in Italy. Subjects’ gender and date of birth and the date of their OPGs were recorded, although the observer was blind to

Results

An ICC of 0.930 (95% C.I.: 0.875–0.964) indicated an acceptable level of intra-observer agreement between paired sets of measurements carried out on re-examined OPGs. Similar results, with an ICC of 0.934 (95% C.I.: 0.870–0.972) were obtained by examining the agreement between the two observers.

Only eight out of 321 subjects over the age of 14 showed some teeth with open apices (S > 0); all the other 313 subjects showed all teeth with closed apices (S = 0). We consequently restricted our analysis of

Discussion

Forensic age estimation is essential in resolving a variety of legal questions, including majority status, criminal liability, and identification of both dead and living individuals. Regression models are the classical and most widely used approaches to address these issues, probably because they are easy to apply and generally produce precise age estimates.

However, it has been noted that such approaches may entail severe bias as regards forensic science, since they systematically overestimate

Acknowledgment

The authors would like to thank Radmedica (Radiologia Odontoiatrica Digitale), Rome, for its efficient support in collecting anonymous, good-quality OPGs.

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