Age Estimation Using Microfocus Xray
The crux of the research was to enhance the method of age estimation based on the ratio of the three dimensional volume of the pulp chamber compared to the total tooth. The main focus of the paper is to utilize a non invasive method like microfocus X-ray computed tomography since destructive approach may not be acceptable in forensic cases due to the loss of evidence.
1.1 Review of Literature
The age estimation can be made with relatively good reliability according to the atrophy of the alveolus, transparence of the dentin, abrasion and the number of missing teeth (Gustafson, 1950). A tendency was observed towards reduced speed of secondary dentin formation in the elderly and in women when the amount of secondary dentin in a tooth has been used as one of several parameters (Solheim, 1992).With the new approach of image acquisition and computer processing system for imaging intact teeth to reconstruct dental images representing segments from different angles of the tooth specimen, quantitatively indicate the optical density, expressing the age dependent pattern of the 3-D anatomy (Sognnaes et al., 1985).There appears to be a definite relationship between the age of an individual and the height and width of the dental pulp chamber, these measures cannot be used as a reliable method of age determination (Prapanpoch et al., 1992).Age estimations based on computerized densitometric analysis were no more accurate than were those determined by caliper measurement; both give a predictive success of ± 5 years in about 45-48% of cases for premolars.
However, television-based digitization system furnishes a more standardized method, a rapid graphic illustration of the results, and an immediate storage of statistical information for future use (Drusini et al., 1991).Teeth provide several useful points about an individual's age. With advancing age, secondary dentine is deposited along the wall of the dental pulp chamber leading to a reduction in the size of the pulp cavity.
These age-related changes can be determined and measured from dental radiographs (Paewinsky et al., 2005).To observe three-dimensional morphological changes with age in the pulp cavities of maxillary first premolar teeth, the decrease in volume was not constant but showed a large decrease between the 20s and the 40s, compared to those of 40s to 60s (Oi et al., 2004).Previous studies states that dental age estimation is developed from two-dimensional dental radiographs, the ratios between the pulpal size and root size and between the pulpal size and tooth size as an enhancement, present study deals with the ratio between three-dimensional pulpal volume and tooth volume. This study is an enhanced work of Aboshi et al (2005) in which the three-dimensional pulp-tooth volume ratio of lower first premolar as a method for age estimation was evaluated by a micro-CT technique.
In an attempt to increase reliability the sample size of lower first premolars was increased, and lower second premolars were also included in this study. In scanning the tooth samples, the micro-CT by cone-beam mode was optimized to save time of the total process (Vandevoort et al., 2004). The ease of use of micro-CT analysis may provide superior voxel data at higher resolution than cone-beam CT analysis.
2 Materials and Methods
Lower premolars were selected for this study as they are less readily damaged by direct heat or traumatic force, than incisors or canines, due to being covered by the soft tissue of the cheek, and are not as easily lost in the dry skull material as single-rooted anterior teeth. Simpler and less diverse root morphology than that exhibited by molar teeth concludes that premolar teeth should have a high potential as a forensic sample for age estimation.The age estimation was achieved by comparing the status of a developing tooth against known population data. Hundred extracted lower first and second premolars from a Japanese population aged 20-78 years were chosen. Differences between teeth from alternate sides of the dental arch are negligible in radiographic adult age determination (Solheim, 1982;1989) so left and right teeth were pooled.
Teeth with any abnormal dental anatomy which might cause difficulty with measurement were excluded. Using a microfocus X-ray computed tomography system (SMX-130CT-SV, Shimadzu, Japan) scanning of the sample was done under the following conditions: tube voltage, 60kV; tube current, 40mA; layer thickness, 63mm; and field of view (XY), 32.4mm. The resolution of one computed tomography tomogram slice was 512X512 pixels. During acquisition, hundreds of two-dimensional projection images through 1808 of rotation were sent in a digital form to the computer memory as a Raw Data.
During reconstruction, the Raw Data containing projection images were reconstructed into axial cross-sections also called slices, in order to gain the third dimension.
2.1 Statistical Analysis
The correlation coefficient measured the agreement between two variables, to evaluate reproducibility or for inter-rater reliability. It was used to assess the consistency, or conformity, of measurements made by multiple observers measuring the same quantity. Since the intraclass correlation coefficient gives a composite of intra-observer and inter-observer variability when used with data where the observers are not exchangeable, its results are sometimes considered difficult to interpret.
Alternative measures such as Cohen's kappa statistic, the Fleiss kappa, and the concordance correlation coefficient might be proposed as more suitable measures of agreement among non-exchangeable observers.Correlation coefficients were evaluated in the paper between age and each predictive variable (PTVR1-4). The correlation coefficient (r) represents the linear relationship between two variables. If the correlation coefficient is squared, then the resulting value (r2, the coefficient of determination) will represent the proportion of common variation in the two variables (i.e., the "strength" or "magnitude" of the relationship).Multiple regression analysis by standard least squares method was used.
The general purpose of multiple regressions was to find the relationship between several independent or predictor variables and a dependent or criterion variable.
The major conceptual limitation of all regression techniques is that it can only ascertain relationships, but never be sure about underlying causal mechanism. It is a seductive technique: "plug in" as many predictor variables as possible and usually at least a few of them will come out significant. This is because capitalizing on chance when simply including as many variables and think of as predictors of some other variable of interest.
This problem is compounded when, in addition, the number of observations is relatively low. The estimates of the regression line are probably very unstable and unlikely to replicate if we were to conduct the study again.A stepwise forward regression method employed was that it searches a large space of possible models. Hence it is open for over fitting the data. This is an automatic procedure for statistical model selection in cases where there were a large number of potential explanatory variables, and no underlying theory on which to base the model selection. The procedure is used primarily in regression analysis, though the basic approach is applicable in many forms of model selection.
This is a variation on forward selection. At each stage in the process, after a new variable is added, a test is made to check if some variables can be deleted without appreciably increasing the residual sum of squares (RSS). The procedure terminates when the measure is (locally) maximized, or when the available improvement falls below some critical value.Stepwise regression procedures are used in data mining, but are controversial. A sequence of F-tests is often used to control the inclusion or exclusion of variables, but these are carried out on the same data and so there will be problems of multiple comparisons for which many correction criteria have been developed.
It is difficult to interpret the p-values associated with these tests, since each is conditional on the previous tests of inclusion and exclusion. The tests themselves are biased, since they are based on the same data (Copas, 1983). Wilkinson and Dalall (1981) computed percentage points of the multiple correlation coefficient by simulation and showed that a final regression obtained by forward selection, said by the F-procedure to be significant at 0.1% was in fact only significant at 5%.
Models that are created may be too-small than the real models in the data (Roecker, 1991).Mean predicting error was calculated and the statistical analyses were performed using the JMP 7 statistical program.
There was no statistically significant intra-observer differences between the paired sets of measurements carried out on the re-examined slice data.
3.1 Pearson's Correlation Coefficients
The ratio at the region of apical one third of the root (L4) correlated least whereas the coronal one third of the root (L2) correlated best with age both in lower first and second premolars. In particular, the variable PTVR4 (ratio between pulp and tooth at apical one third of root) in lower first premolar alone was not significantly correlated with age while all other variables in lower first and second premolars were significant and negative (Table 2).
From figure 2, the coronal one third of the root (L2) showed the greatest ratio values, followed by L3, L4 and L1 in all age groups. The most marked reduction in volume ratio was observed between the 20s and the 50s at L2 in both lower first and second premolars, and between the 20s and the 30s at L3 in lower first premolars. All other results showed moderate reduction over all age groups at L1 and L4 both in lower first and second premolars.
3.2 Multiple Regression Analysis
Only PTVR2 showed a significant correlation at P < 0.001, both in lower first and second premolars. The values of R2 and R*2 were 0.635 and 0.602 for lower first premolars, and 0.703 and 0.677 for lower second premolars.Only the variable of PTVR2 was chosen for lower first premolars whereas the two variables of PTVR1 and PTVR2 were chosen for lower second premolars. The regression models for lower first premolars (1) and lower second premolars (2), utilizing the chosen variables, yielded the following linear regression formulae:The accuracy for each model was shown in Table 6. The model (1) for lower first premolars with the PTVR2 variable explained 62.5% (R2 = 0.625) of the total variance. The model (2) for lower second premolars with two variables explained 69.8% (R2 = 0.698) of the total variance as well. The values of R*2 were 0.617 for lower first premolars and 0.685 for lower second premolars.
In the full model using all variables, the values of R*2 were smaller than those in the stepwise model both in lower first and second premolars.The root mean square errors (RMSE) were 9.07 for lower first premolars and 8.25 for lower second premolars. These values decreased compared to those in the full models using all variables. The accuracy of the method were ME = 7.29 years for lower first premolars and 6.32 years for lower second premolars.Both residual plots (Figs. 3 and 4, left) show no obvious pattern and the actual versus estimated plots (Figs.
3 and 4, right) show that the regression model fits the trend of the data fairly well.
The author contradicts the work from the previous studies in three ways:In previous studies of dental age estimation from two-dimensional dental radiographs, the ratios between the pulpal size and root size and between the pulpal size and tooth size have been successfully used. The ratio between three-dimensional pulpal volume and tooth volume has been chosen as a possible age indicator in the present study.This study followed previous research (Aboshi et al., 2005) in which the three-dimensional pulp-tooth volume ratio of lower first premolar as a method for age estimation was evaluated by a micro-CT technique.
In an attempt to increase reliability the sample size of lower first premolars was increased, and lower second premolars were also included in this study.The total procedure including scanning, image reconstruction, pre-processing and post-measurement took approximately 3 h per tooth, which was faster than the 5 h per tooth processing time in the previous report (Vandevoort et al., 2004) on a micro-CT, but slower than 1 h on cone beam CT (Someda et al., 2009).The mean values of the pulp-tooth volume ratio by age groups indicated a general inverse relationship between age and the ratio with the steepest reduction occurring in the 20-50 age groups, and most noticeably at the L2 level. In this study the highest correlation with age was observed for the pulp-tooth volume ratio in the coronal one third of root (L2) both in lower first and second premolars. This reinforced the previous results (Aboshi et al., 2000;2001) using two dimensional measurements as age-related variables.
The coronal one third of root pulp may be most stable region in lower premolars with minimal morphological diversity and few influences from attrition and other post-development changes. This stability should lead to few measurement errors and interobserver bias.To determine the variables with the most significant influence on age estimation, a stepwise regression procedure was applied with inclusion level at P < 0.05, not excluding variables with any significant contribution to the regression. Only the variable of PTVR2 was chosen as an independent variable for lower first premolars, whereas PTVR2 and PTVR1 were chosen for lower second premolars.
Only the variable PTVR2 showed significance (P < 0.001). There was a tendency towards a weaker correlation with age in the apical root region than in the cervical root region. This study investigated the relationship between age and age related changes in pulp-tooth volume ratio at different levels of lower premolars and the best correlation with age was found in the coronal one third of the root.
The root and its pulp cavity may remain for examination after trauma and fire. Pulp-tooth volume ratio is an age-dependent variable that can be used to estimate age with reasonable accuracy.
It will also be needed to determine the regression equations with higher accuracy by investigating other indicators for age-related changes in teeth including teeth with known abnormalities.Someda et al. (2009) used a large sample of mandibular central incisors, which have the lowest morphological diversity, and made sex- or region-specific equations with high accuracy (males, R2 = 0.67; females, R2 = 0.76). Although a gender-specific analysis has not been performed in this study, the accuracy of equations is similar to those of Someda's group who used pulp/tooth volume ratios as variables but could be done future.
The result of multiple regression analysis by stepwise method provided good support for the use of pulp-tooth volume measurements by microfocus X-ray computed tomography of lower premolars in age estimation.
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