Journal of Forensic Dental Sciences
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ORIGINAL ARTICLE
Year : 2016  |  Volume : 8  |  Issue : 1  |  Page : 56-57  

Age estimation using intraoral periapical radiographs


1 Department of Oral Medicine and Radiology, Dr. D.Y Patil Dental College and Hospital, Navi Mumbai, Maharashtra, India
2 Department of Oral and Maxilloafacial Pathology, Maharana Pratap Dental College and Hospital, Kanpur, Uttar Pradesh, India

Date of Web Publication18-Feb-2016

Correspondence Address:
Pooja S Rajpal
B.3 Sudhama Sadan, Dr. Rajendra Prasad Road, Mulund West, Mumbai - 400 080, Maharashtra
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0975-1475.176955

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   Abstract 

Context: Changes in the size of dental pulp caused by the apposition of secondary dentin and occlusal wear are morphometric parameters for estimating age. Aim: To estimate the accuracy of age evaluation by Kvaal's method and the effect of occlusal wear on age using digital intraoral periapical radiographs in a subset of the Indian population. Materials and Methods: A total of 300 teeth were radiographically evaluated using intraoral periapical digital radiographs from 50 adult patients. A few modifications were made in the design of the study compared to the original Kvaal's method. The radiographs of three teeth from each jaw were taken and morphometric measurements in ratios were analyzed, which included the pulp length to tooth length (X1), pulp length to root length (X2), pulp width to root widths at three defined levels (X3), and tooth length to root length (X4). Statistical Analysis: The Pearson product-moment correlation coefficient (PCC) between age and the morphological variables showed that among them X1, X2, and X3were statistically significant but not the tooth root length ratio (X4). Conclusions: The ratios X1, X2, and X3were good indicators of age and hence a multiple linear regression model for age estimation was derived using these three variables. However, it was found that X4was not a good indicator of age estimation in said population.

Keywords: Age estimation, dental radiographs, noninvasive, pulpal reduction, secondary dentin


How to cite this article:
Rajpal PS, Krishnamurthy V, Pagare SS, Sachdev GD. Age estimation using intraoral periapical radiographs. J Forensic Dent Sci 2016;8:56-7

How to cite this URL:
Rajpal PS, Krishnamurthy V, Pagare SS, Sachdev GD. Age estimation using intraoral periapical radiographs. J Forensic Dent Sci [serial online] 2016 [cited 2019 Oct 15];8:56-7. Available from: http://www.jfds.org/text.asp?2016/8/1/56/176955


   Introduction Top


Developmental changes and regressive changes of the tooth have been related to chronological age in adult and subadult populations.[1],[2]

The underlying concept of the study was that pulpal reduction caused by apposition of secondary dentin and occlusal tooth wear are used as morphometric parameters in estimating age.[3],[4]

The timing of the secondary dentin formation is fit by a curved line rather than a straight line with underlying chronological differences. Hence, there is a need for research to provide sufficient data for age estimation.[5]

The aim was thus to estimate the accuracy of age evaluation by the analysis of measurements of dental pulp and effects of occlusal wear using digital intraoral periapical radiographs in a subset of the Indian population.


   Materials and Methods Top


Kvaal et al. in 1995[6] reported a method that allows age estimation based on the morphological measurements of two-dimensional radiographic features of individual teeth. The measurements include comparisons of pulp length and tooth length (X1), pulp length and root length (X2), pulp and root widths at three defined levels (X3) and tooth length and root length (X4). Our study of age estimation was based on the concept as given by Kvaal et al.,[6] although a few changes in the study design were made to assess whether the accuracy of age estimation can be influenced.

50 patients, ages 15-57 years old and each with known chronological age, were randomly selected irrespective of their religion or gender [Table 1]. Each patient's chronological age was noted after verifying his/her respective identity proof. Informed consent was obtained from all the patients. The institutional ethical committee approved the protocol of this study.
Table 1: Age and gender distribution of the study sample

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Six teeth were selected for each patient: One maxillary central incisor, one maxillary lateral incisor, one maxillary second premolar, one mandibular central incisor, one mandibular lateral incisor, and one mandibular second premolar. The teeth were selected from the right or the left side randomly.[6] The examined teeth had to be in normal functional occlusion and free from any manifestations of traumatic results. Furthermore, teeth with fillings, crowns, and carious lesions were excluded from evaluation. Teeth with pathologies in the apical bone, and rotated or endodontically treated teeth were also excluded.

High-quality digital intraoral periapical radiographs with respect to contrast and angulation were made at an exposure of 10 mA and 70 kVp using the Satelec X-mind intraoral x-ray machine (Acteon Company, Italy) and the Kodak RVG 5000 digital radiography system (Eastman Kodak Company, Rochester, NY). Paralleling technique was used, employing the Rinn XCP-DS positioning device (Dentsply International Inc. USA). All radiographs were obtained in DICOM format and analyzed using Kodak Dental Imaging Software Windows v6.0.1 software, (Eastman Kodak Company, Rochester, NY) which gave a digital quantification of measurements between any two reference points with a resolution of ≥ 14 lp/mm.

The following measurements were then made from the radiographs using the Kodak RVG 5000 digital radiography system on all six teeth from each patient: [Figure 1]
Figure 1: Diagram showing measurements: Tooth length (T); Pulp length (P); Root length (R); Root and pulp width at the cementoenamel junction (CEJ) (W1); Root and pulp width midway between CEJ and apex (W2); Root and pulp width at apex (W3)

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  • The maximum tooth length—from the occlusal surface to the apex of the tooth (T)
  • The pulp length—from the highest pulp horn to the radiographic apex (P)
  • The root length on the mesial surface—from the cementoenamel junction (CEJ) to the root apex (R),
  • The root and pulp width at three levels—the levels being at the CEJ (W1), at the midroot level (W2), and the apex of the root (W3); then a mean width was derived.


The following ratios were then calculated from the above measurements:

  • The ratio between the lengths of the pulp and the tooth (X1)
  • The ratio between the lengths of the pulp and the root (X2)
  • The ratio between the mean widths of the pulp and the root (X3)
  • The ratio between the lengths of the tooth and the root (X4).


The ratios of measurements were used rather than the measurements directly in the analysis in order to reduce the effect of a possible variation between the magnification and angulations of the radiographs.[6]

A single observer carried out all the measurements. To test the intraobserver reproducibility, a random sample of 30 radiographs was reexamined after a week.

All four morphological ratios X1, X2, X3, and X4 were used as variables for age estimation in the statistical analysis. The Pearson product-moment correlation coefficient (PCC) was evaluated between chronological age and the predictive variables. Multiple regression analysis was then made, employing age as the dependent variable and the predictive variables as the independent variables. Besides evaluating the age using measurements from all six teeth, we also evaluated separate predictions restricted exclusively to either the maxillary or mandibular teeth.


   Results Top


Patients were selected from the age group of 15-57 years, with a mean age of 34.9 years. The patients included 27 males and 23 females [Table 1].

PCC between age and the morphological variables were significant except for X4 [Table 2]. The correlation coefficient between age and tooth root length ratio was poor with the P value of 0.8973 showing less significance, indicating that occlusal wear is not well correlated with age.
Table 2: Pearson product-moment correlation coefficient

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From the calculated mean values, standard deviation (SD) and standard error of mean (SEM) between the chronological age and the predicted age were determined, as presented in [Table 3].
Table 3: Descriptive statistics

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[Figure 2],[Figure 3] and [Figure 4] show the scatterplot diagrams between the chronological age and the predicted age when all six teeth, three maxillary teeth and three mandibular teeth, were used respectively.
Figure 2: Plots of observed age against the predicted age using the regression model for all six teeth

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Figure 3: Plots of observed age against the predicted age using the regression model for three maxillary teeth

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Figure 4: Plots of observed age against the predicted age using the regression model for three mandibular teeth

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The relationship between these variables and the dependent variable could be expressed as Y = A + B1 X 1 + B2 X 2 + B3 X 3+….BnXn + E.

Where Y = predictive dependent variable value

A = Value of Y when all X s are zero

X1, X2, X3 = Independent variables

B = Coefficients corresponding to the independent variables

n = The number of independent variables

E = An error term

The predictive variables X1, X2, and X3 were used as an input dataset in the multiple regression analysis, yielding the following regression formulas. It should be noted that X4 was not included in the multiple regression analysis, as it did not correlate significantly with age [Table 4] and [Table 5]
Table 4: Multiple regression analysis predicting chronological age from chosen predictors

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Table 5: Multiple regression analysis

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.

Equation 1: When all six teeth were considered together

Age = -161.04 X 1+ -28.30 X 2+ -191.59 X 3 + 236.98 (+/-6.42)

Where X1, X2, and X3 are the mean values

The coefficient of determination (R2) for all six teeth was 0.7381, with a standard error of estimate of 6.42 years (F = 43.272).

F: F Statistic

Analysis was done for only the maxillary teeth and the regression equation obtained was as follows:

Equation 2:

Age = -175.18 X 1+-8.85 X 2 + -127.96 X 3 + 211.89(+/-7.31)

Where X1, X2, and X3 are the mean values

The coefficient of determination (R2) for only maxillary teeth was 0.6608, with a standard error of estimate of 7.3 years (F = 29.87).

Analysis was done for only the mandibular teeth and the regression equation obtained was as follows:

Equation 3:

Age = -167.89 X 1+ -51.55 X 2+ -151.32 X 3 + 265.60 (+/-7.85)

Where X1, X2, and X3 are the mean values

The coefficient of determination (R2) for only mandibular teeth was 0.6088, with a standard error of estimate of 7.8 years (F = 23.865).

A higher correlation coefficient was obtained when all the six teeth were included.


   Discussion Top


Dental age estimation requires the use of morphologic, radiographic, histological, and biochemical methods to estimate age-dependent changes in teeth. For age estimation, different methods are available; however, invasive methods using extracted teeth, ribs, and femurs cannot be used in living individuals. Assessment of sexual and skeletal maturation, radiological examination of bones, and also clinical and radiological examination of the dentition are noninvasive ways to determine age. Dental age estimation can be based on different properties of the dentition. Age estimation methods employ various forms of tooth modification, including tooth wear,[7],[8] root dentin transparency,[9],[10] tooth cementum annulation,[11] racemization of aspartic acid,[12] and apposition of secondary dentin.[6],[13]

Gottlieb was the first person to correlate changes in dentition with age.[14] In 1925, Bodeckar also established that the apposition of the secondary dentin was correlated with age.[15] The secondary dentin is laid down by the odontoblasts throughout a person's life, causing a reduction in the size of the pulpal cavity.[16] Secondary dentin deposition was introduced for age estimation in the method by Gustafson, so that secondary dentin was one of the parameters in addition to attrition, periodontal recession, cementum apposition, apical translucency, and external root resorption.[17]

Presently, there is no evidence that the process of secondary dentin formation occurs in a linear manner, or that every age group needs the same time span to present itself with a defined amount of secondary dentin. Although linear regression is widely used in forensics to provide the estimate of the measurement, for instance the age at death or the living stature, it should be kept in mind that human growth is not a linear process.[18] The quality of secondary dentin deposition is also influenced by factors like race, ethnicity, diet, and lifestyle. Authors have highlighted the need for population-specific formulas due to differences in ethnicity to achieve precise and accurate results.[19],[20],[21]

A study of radiographs of teeth is a nondestructive simple method to obtain information and is a technique used daily in dental practice. The advent of digital radiography has increased the quality of the images as also the ease of measurements and maintenance of records. The accuracy of digital method has been proven in intraoral radiography.[22] In the present study, the selection of the dental parameters used for age assessment was based on their implementation in dental practice and on their reproducibility.

This study was based on the concept established by Kvaal et al.[6] Reviewing the literature, we found that age estimation using Kvaal's method showed varied results, some underestimating the exact age by approximately 30 years, whereas some were as close as 8-9 years.[18],[23] Hence, we designed a study with a few modifications. Multirooted teeth and canines have not been good predictors for the determination of age.[6],[23] Thus, we selected the maxillary central incisor, maxillary lateral incisor, maxillary second premolar, mandibular central incisor, mandibular lateral incisor, and mandibular second premolar for determining age for our study. In addition, the pulp root width was measured at three different levels, namely at the CEJ, at the midroot level, and at the apex. Correlation coefficients for X1, X2, and X3 were statistically significant, indicating that the ratios decrease with increasing age.

However, the correlation coefficient for X4 was poor for all types of teeth, with the P value of 0.8973, showing less significance. A possible explanation could be that the whole length of the tooth was measured instead of only the crown, which has been shown to be strongly correlated with age.[24]

Statistically significant values were noted when maxillary or mandibular teeth were used alone. As in other studies, a higher correlation coefficient was obtained when all the six teeth were included, indicating that the more the information gained from the patient, the greater are the chances of an accurate age estimation; in addition, it would also reduce the effect of unusual anatomy of any one tooth.

In the present study, X1 showed the strongest correlation compared to the width ratios as depicted in the study conducted by Kvaal et al.[6]

A study conducted using conventional orthopantomograms [18] using the equations of Kvaal et al. has shown a mean underestimation of age ranging 38-47 years when three to six teeth were included, whereas a study done on digital orthopantomograms [23] showed an estimation of ± 8.3 years in an Indian population. Patil et al. concluded a standard error of estimate of 6.5 years with a modified Kvaal's formula on a sample of the Indian population.[19]

The accuracy of age estimation in this study was ± 6.42 years when all six teeth were included. The better accuracy may be due to the better resolution of digital intraoral periapical radiographs compared to orthopantomograms and also because of the exclusion of multirooted teeth and canines. Furthermore, we employed the paralleling technique, which helps in reducing angulation and technique errors and has better reproducibility.

Some authors [18] have indicated an inapplicability of the regression equations of Kvaal et al.[6] and Pawensky et al.[13] in younger populations. Our results, however, differ, as we could estimate the age in a population ranging 15-57 years old with an overall accuracy of ± 6.4-7.8 years with the design employed in our study using the Kvaal's method. From this study, we have derived regression equations for age estimation in the Indian population.

It is also important to emphasize that any methodological approach to age assessment of a living individual establishes the physiological age, and that sexual, dental, or skeletal development is representative of the overall physical maturity and not chronological age.[25],[26] A careful approach to age determination is necessary, taking into consideration the influence of pathological conditions, ethnicity and gender variation. Therefore, comprehensive approaches to age estimation, which considers multiple maturity indicators, are superior to those which use non comprehensive methods.[25],[27]


   Conclusion Top


We conclude that the ratios X1, X2, and X3 are good indicators of age, while X4 is not correlated with age estimation in said population. According to the authors, the results of this study indicate that Kvaal's method is a reliable method to estimate age in both young and older populations. With a few modifications of Kvaal's method, we could estimate age with a standard error of estimate of ± 6.4-7.8 years in a sample of the Indian population. Only limited conclusions can be drawn from a single study. Because of the small sample size of this study, we are conservative in our interpretation of the results. However, further studies can be carried out using the regression formulas derived in this study.

 
   References Top

1.
Vishwanath R, Rao B, Lele S, Mamatha GP, Shashikanth. Estimation of age by measuring the root length of mandibular premolars and permanent mandibular molars - A digital radiographic technique. J Indian Acad Oral Med Radiol 2004;16:113-8.  Back to cited text no. 1
  Medknow Journal  
2.
Thorson J, Hägg U. The accuracy and precision of the third mandibular molars as an indicator of chronological age. Swed Dent J 1991;15:15-22.  Back to cited text no. 2
    
3.
Solheim T. Amount of secondary dentin as an indicator of age. Scand J Dent Res 1992;100:193-9.  Back to cited text no. 3
    
4.
Cameriere R, Ferrante L, Belcastro MG, Bonfiglioli B, Rastelli E, Cingolani M. Age estimation by pulp/tooth ratio in canines by mesial and vestibular peri-apical x-rays. J Forensic Sci 2007;52:1151-5.  Back to cited text no. 4
    
5.
Woods MA, Robinson QC, Harris EF. Age-progressive changes in pulp widths and root lengths during adulthood: A study of American blacks and whites. Gerodontology 1990;9:41-50.  Back to cited text no. 5
    
6.
Kvaal SI, Kolltveit KM, Thomsen IO, Solheim T. Age estimation of adults from dental radiographs. Forensic Sci Int 1995;74:175-85.  Back to cited text no. 6
    
7.
Yun JI, Lee JY, Chung JW, Kho HS, Kim YK. Age estimation of Korean adults by occlusal tooth wear. J Forensic Sci 2007;52:678-83.  Back to cited text no. 7
    
8.
Kim YK, Kho HS, Lee KH. Age estimation by occlusal tooth wear. J Forensic Sci 2000;45:303-9.  Back to cited text no. 8
    
9.
Lamendin H, Baccino E, Humbert JF, Tavernier JC, Nossintchouk RM, Zerilli A. A simple technique for age estimation in adult corpses: The two criteria dental method. J Forensic Sci 1992;37:1373-9.  Back to cited text no. 9
    
10.
González-Colmenares G, Botella-López MC, Moreno-Rueda G, Fernández-Cardenete JR. Age estimation by a dental method: A comparison of Lamendin's and Prince and Ubelakar's technique. J Forensic Sci 2007;52:1156-60.  Back to cited text no. 10
    
11.
Wittwer-Backofen U, Gampe J, Vaupel JW. Tooth Cememtum annulation for age estimation: Results from a large known-age validation study. Am J Phys Anthropol 2004;123:119-29.  Back to cited text no. 11
    
12.
Ohtani S. Studies on age estimation using racemization of aspartic acid in cementum. J Forensic Sci 1995;40:805-7.  Back to cited text no. 12
    
13.
Paewinsky E, Pfeiffer H, Brinkmann B. Quantification of secondary dentine formation from orthopantomograms-a contribution to forensic age estimation methods in adults. Int J Legal Med 2005;119:27-30.  Back to cited text no. 13
    
14.
Brkić H, Miličević M, Petrovečki M. Forensic determination of dental age of adults. Acta Stomatol Croat 2008;42:267-72.  Back to cited text no. 14
    
15.
Bodeckar CF. A consideration of some of the changes in the teeth from young to old age. Dental Cosmos 1925;67:543-9.  Back to cited text no. 15
    
16.
Morse DR. Age-related changes of the dental pulp complex and their relationship to systemic aging. Oral Surg Oral Med Oral Pathol 1991;72:721-45.  Back to cited text no. 16
    
17.
Gustafson G. Age determination of teeth. J Am Dent Assoc 1950;41:45-54.  Back to cited text no. 17
[PUBMED]    
18.
Meinl A, Tangl S, Pernicka E, Fenes C, Watzek G. On the applicability of secondary dentin formation to radiological age estimation in young adults. J Forensic Sci 2007;52:438-41.  Back to cited text no. 18
    
19.
Patil SK, Mohankumar KP, Donoghue M. Estimation of age by Kvaal's technique in sample Indian population to establish the need local Indian-based formulae. J Forensic Dent Sci 2014;6:166-70.  Back to cited text no. 19
[PUBMED]  Medknow Journal  
20.
Cunha E, Baccino E, Martrille L, Ramsthaler F, Prieto J, Schuliar Y, et al. The problem of aging human remains and living individuals: A review. Forensic Sci Int 2009;193:1-13.  Back to cited text no. 20
    
21.
Babshet M, Acharya AB, Naikmasur VG. Age estimation in Indians from pulp/tooth area ratio of mandibular canines. Forensic Sci Int 2010;179:125.e1-4.  Back to cited text no. 21
    
22.
Kullman L, Matrinsson T. The accuracy of measuring tooth length from intraoral radiographs using computerized registration. Dentomaxillofacial Radiol 1998;17:105-7.  Back to cited text no. 22
    
23.
Limdiwala PG, Shah JS. Age estimation by using dental radiographs. J Forensic Dent Sci 2013;5:118-22.  Back to cited text no. 23
[PUBMED]  Medknow Journal  
24.
Smith BG. Robb ND. The prevalence of tooth wear in 1007 dental patients. J Oral Rehabil 1996;23:232-9.  Back to cited text no. 24
    
25.
Santoro V, De Donno A, Marrone M, Campobasso CP, Introna F. Forensic age estimation of living individuals: A retrospective analysis. Forensic Sci Int 2009;193:129.e1-4.  Back to cited text no. 25
    
26.
Maples WR. The practical application of age estimation techniques. In: Iscan MY, editor. Age Markers in the Human Skeleton. Springfield, IL: Charles C. Thomas Publishers; 1989. p. 319-24.  Back to cited text no. 26
    
27.
Introna F, Campobasso CP. Biological vs. legal age of living individuals. In: Schmitt A, Cunha E, Pinheiro J, editors. Forensic Anthropology and Medicine: Complementary Sciences from Recovery to Cause of Death. Totowa, NJ: Human Press Inc.; 2006. p. 238-9.  Back to cited text no. 27
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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