|Year : 2014 | Volume
| Issue : 4 | Page : 398-404
Coronal pulp biomarker: A lesser known age estimation modality
Smrithi D Veera1, Jayanthi Kannabiran2, Nagarathna Suratkal3, Dayananda Bagur Chidananada4, Kumar Raghav Gujjar5, Suresh Goli1
1 Department of Oral Medicine and Radiology, DJ Dental College and Hospital, Modinagar, Uttar Pradesh, India
2 Department of Oral Medicine and Radiology, BIDS Dental College and Hospital, Bangalore, Karnataka, India
3 Department of Periodontia, Shree Mookambika Institute of Dental Sciences, Kanyakumari, Tamil Nadu, India
4 Department of Oral Pathology and Microbiology, RKDF Dental College and Hospital, Bhopal, Madhya Pradesh, India
5 Department of Dentistry, SEGi University, Kota Damansara, Selangor, Malaysia
|Date of Submission||10-Jul-2014|
|Date of Acceptance||28-Feb-2015|
|Date of Web Publication||22-Apr-2015|
Smrithi D Veera
8/2-5, 20th Main, 1st Stage, BTM Layout, Bangalore - 560 029, Karnataka
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Introduction: The evolving state of art digital technology currently available is opening new avenues in forensic odontology for age estimation methods which are subject to debate in terms of accuracy and precision. A study was carried to analyze efficacy and practical application for age estimation using digital panoramic radiographs on South Indian population. Aims and Objectives: 1. To study reduction of coronal pulp chamber using Tooth Coronal Index (TCI) on panoramic radiographs and correlate with chronologic age. 2. To establish accuracy of digital panoramic radiographs as a simple, non-invasive tool. Materials and Methods: The study illustrates the potential value of a little known aging method. The study groups comprised a total of 100 subjects of both sexes in age range of 20 and 60 years each who were subjected to panoramic radiography. A panoramic radiographic examination using digital panoramic machine was conducted on selected individuals. The TCI was calibrated using AGFA computer software for accuracy and precision. The values obtained were subjected to regression analysis, results calculated and correlated with chronologic age. In the present study a population of known age was studied and subjected to digital panoramic radiographic examination. The correlation between reduction of coronal pulp cavity and chronological age was examined. TCI was computed for each tooth and regressed on real age. Statistical Analysis Used: Pearson correlation co-efficient was used to find the significance of relationship between age and TCI. Regression analysis has been used for predicting age using TCI for premolar and molar. Inaccuracy and bias have been determined to assess the precision of prediction equations. Results and Conclusion: Prediction potential of TCI comes down for ages above 50 years and is comfortably good below 50 years without much difference between premolars and molars. This study demonstrates the potential value of TCI for age estimation.
Keywords: Computer-based image analysis, coronal biomarker, forensic odontology, pulp cavity height, tooth coronal index
|How to cite this article:|
Veera SD, Kannabiran J, Suratkal N, Chidananada DB, Gujjar KR, Goli S. Coronal pulp biomarker: A lesser known age estimation modality. J Indian Acad Oral Med Radiol 2014;26:398-404
|How to cite this URL:|
Veera SD, Kannabiran J, Suratkal N, Chidananada DB, Gujjar KR, Goli S. Coronal pulp biomarker: A lesser known age estimation modality. J Indian Acad Oral Med Radiol [serial online] 2014 [cited 2023 Feb 8];26:398-404. Available from: http://www.jiaomr.in/text.asp?2014/26/4/398/155684
| Introduction|| |
In the recent past, forensic odontology has shown increasing interest in search for optimal age estimation procedures. Teeth form a very unique and suitable parameter for dental age estimation for many reasons. , The development of dental tissue is less affected by endocrine diseases or nutritional variations than other tissues. Once a tooth is fully mineralized and erupted, it forms a very stable entity. Both the developmental and regressive changes affecting the teeth can be related to chronological age. Furthermore, a tooth will resist the influence of many factors and disintegrates very slowly and is impervious to most types of injury. Also, most people would have been to a dentist in their lifetime, henceforth, the ante-mortem records of dental tissues will be available to match with the victim with a high probability.
Age estimation rooted on tooth morphology is broadly categorized as morphologic and radiological methods.  The former are further sub-classified as clinical, histological and biochemical examination. Radiological procedures offer certain advantages over morphologic counterparts, most importantly by allowing both in vitro as well as in vivo studies. , Radiographical examination is useful because formation of secondary dentin and changes in the size of pulp can be measured. Size of the pulp chamber, which is expected to reduce with age, may show a consistent relationship with chronological age.
Most of the studies use conventional radiographic methods for age estimation from dental pulp. Conventional radiographic methods pose a variety of problems such as processing errors, storage for longer duration and image transfer. Also, of significant disadvantage of the conventional radiography is that measurements are difficult to make. Dental digital radiography became available to the dental profession in the late 1980s. Advances in silicon technology and software development are making its use more and more prevalent and practical. Software tools that accompany these digital systems allow measurements to be made with ease and rapidity. Digital radiographs with software tools permit metric measurements of morphological parameters of teeth which are relatively precise. ,,,, In addition to this, computer-assisted image analysis, avoids the bias inherent in observer subjectivity. It also improves the reliability and consequently the statistical analysis of the data. It is, therefore, highly pertinent to device a simple cost-effective study to perform morphometric analysis of dental pulp utilising the digital radiographic method which may have a bearing on estimation of age in individuals.
| Materials and Methods|| |
The study groups comprised 100 subjects, of both sexes in the age scale of 20-60 years from the South Indian patients attending the department of Oral Medicine and Radiology. A 1-year prospective study was undertaken on 50 male and 50 female patients selected randomly after fulfilling the set criteria. Ethical clearance was taken from the local review board. The inclusion and exclusion criteria were as follows:
The patients included in the study were:
- Healthy volunteers aged between 20 and 60 years, who had come for routine dental treatment and were subjected to panoramic radiography.
- Panoramic radiographs showing good morphological features of the study teeth.
- Study teeth with functional antagonists.
Exclusion criteria ,,,,
The patients with the following were excluded from the study:
- Individuals who had undergone prolonged corticosteroid therapy.
- Individuals who had severe systemic illness such as vitamin D-resistant rickets, and dentinogenesis imperfecta associated with osteogenesis imperfecta.
- Individuals with multiple caries, severe periodontitis, multiple tooth loss and retained deciduous teeth, or teeth with fillings, crowns, root canal treatment, and teeth supporting removable or fixed prosthesis.
- Teeth with severe wasting diseases.
- Pregnant ladies and those not willing for radiographic procedures.
The 100 random randomly patients (50 each from both the sexes) who had come for dental-related problems and some volunteers, having fulfilled the above required clinical criteria were subjected to a radiographic investigation for the study after obtaining their informed consent. A panoramic radiographic examination was performed using digital panoramic machine (PM 2002 EC Proline) and the second premolar and first molar wherein the pulp chamber exhibited good delineation were evaluated. The mandibular right dentition was arbitrarily considered. The difference between upper and lower dentition and the teeth sides are negligible in radiographic adult age determination as witnessed in previous studies. 
Exposure parameters were: ,, 68 kVp, 8 mA, 18 seconds exposure time. On exposure, the radiographic image was displayed almost instantly on the computer monitor. The tooth number (FDI system of tooth numbering) was specified on the chart provided by the software. This allowed correct orientation of the captured image in the appropriate quadrant. The images so captured were stored in the folders created for different age groups. Relevant demographic data was entered as text along with the image.
The height (mm) of the crown (CH = Coronal height) and the height (mm) of the coronal pulp cavity (CPCH = Coronal pulp cavity height) was measured. For the selected teeth measurement, a straight line (cervical line) was traced between the cemento-enamel junctions (which is the division between the anatomical crown and root). The CH was measured vertically from this cervical line to the tip of the highest cusp according to Moss et al.  [Figure 1], [Figure 2], [Figure 3]. The CPCH was measured vertically from the cervical line to the tip of the highest pulp horn according to Ikeda et al.  [Figure 1], [Figure 4] and [Figure 5]. This measurement provides the tooth coronal index (TCI) ,,, for each tooth which was then calculated as follows:
TCI = CPCH × 100/CH
|Figure 1: Schematic representation of measurements taken off a panoramic radiograph with a digital caliper to 0.01 mm|
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The measurements were displayed in millimeters along with the captured image for further identification and reference. Each radiograph depicting the two teeth provided four pulp measurements altogether. The TCI was calibrated ,, with the AGFA (Agfa-Gevaert Group) computer software for accuracy and precision which was further subjected to regression analysis. Also, intra observer variability of the three readings of the measurements was noted at a 1-week interval with no significant difference. The result was next calculated and correlated with the chronologic age for appraisal.
For the CH, CPCH and TCI between male and female individuals, Student 't' was employed. Pearson correlation co-efficient was used to find the significance of relationship between age and TCI. Regression analysis was used for predicting the age using the TCI for premolars and molars. Inaccuracy and bias was determined to assess the precision of the prediction equations.
| Results|| |
A correlation study consisting of subjects was undertaken to examine the level of affiliation of TCI with age in premolars and molars as well as setting up the equation of age prediction based on TCI. Percentage of each age group depiction is revealed in [Table 1] and [Figure 6]. The mean value of CH, CPCH and TCI in premolars (P CH = 0.534, P CPCH = 0.785, P TCI = 0.658) as well as molars (P CH = 0.273, P CPCH = 0.129, P TCI = 0.679) between males and females were insignificant statistically. As the CH, CPCH and TCI was not statistically relevant between male and female subjects in both premolar and molar, the prediction equation was made on the combined samples as graphically represented in [Table 2] and [Figure 7].
|Table 2: Comparison of CH, CPCH and TCI in male and female subjects. Results are presented in Mean ± SD (Min-Max)|
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The CH value in premolar and molar is appreciably changing with age with higher CH value in senior age set (P CH = 0.015). Similarly, CPCH and TCI values for premolar and molar is notably linked to age with considerable decline of CPCH and TCI in advanced age assemblage (P CPCH ≤ 0.001) as graphically symbolized in [Table 3] and [Figure 8].
|Table 3: Comparison of CH, CPCH and TCI in different age groups of subjects studied. Results are presented in Mean ± SD|
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The correlation coefficient is truly exceptional between the age and TCI in premolars and molars (>0.9) in the age section of 21-30 years, 31-40 years and 41-50 years, where as a poorer correlation coefficient was observed in the higher age section i.e., 51-60 years (0.8) as seen in [Table 4]. The correlation in different age segments were significantly high with both premolars and molars but the difference between premolars and molars is not statistically noteworthy. Hence, it is envisaged that TCI can be good predictors of age in both premolar as well as molar. The comparison data of Pearson correlation of TCI and age in premolars and molars is depicted in [Figure 9]a and b.
|Figure 9: (a) Pearson correlation of TCI and age in premolars (b) Pearson correlation of TCI and age in molars|
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|Table 4: Pearson correlation of age with TCI in different age groups for premolars and molars|
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Prediction prospective of TCI for calculating age slides down above 50 years in both premolars and molars but is tremendous in age faction below 50 years as seen in [Table 5]. However, there was no credible disparity of determination co-efficient between premolar and molar. The discrepancy between the real and predicted age for the premolars for the combined sample was 7 months with a bias of 0.0 and for the molars it was 9 months with a 0.0 bias as presented in [Table 6].
|Table 5: Regression analysis to predict the age using TCI as predictors in premolars and molars for the subjects in different age groups|
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| Discussion|| |
Panoramic radiography method for adult age assessment is very rarely the accredited choice. This study measures the chronological age with morphometric values of pulp on digital panoramic radiographs for the mandibular second premolar and first molar. The panoramic technique ,, has the advantage of displaying all the mandibular and maxillary teeth on a single film. The computer-assisted image analysis avoids the bias inherent in observer subjectivity, which supplies a relatively exact method of measurements, improves reliability and consequently the statistical data analysis.
Chronological age computation ,,, is accomplished by determining the ratio of coronal pulp height to that of the coronal height. The ratio obviates the need to standardize tooth sizes, so dental radiographs obtained with different techniques may be used. The strength of the ratio correlation is superior when weighed against root dentin transparency on a sample of teeth from the same population. The current study presents analogous results as demonstrated by Drusini et al. (1997) on the skeletal remains of 100 year-old skeletons with an error of ± 5 years in 70.37% of cases.
The prediction equation in molar and premolar using TCI as forecaster is 99.0% and 99.2% respectively [Figure 9]a, b and [Table 5] which is in agreement with other studies. , In the study by Kvaal,  the TCI correlation coefficients ranged from r -0.650 to -0.799 and were significant in both gender and in premolars and molars (P < 0.01), which allowed age estimation with an error of ± 5 years. Pulp chamber size was assessed using bitewing radiographs for molars and premolars and significant reduction in height of the pulp chamber of the first permanent molar was found. In consequence, the study substantiates a meaningful correlation of age with TCI.
The present study shows age correlation and TCI in different age groups are sizeable in both premolars and molars but the difference in correlation between premolar and molar is statistically inconsequential, in contrast to the study by Drusini et al. (1997), which shows molar equation for males and premolar equation for female and combined samples to estimate age better. The present study demonstrates that either the molars or the premolars may be used as accurate adult age predictors similar to other studies.
The high correlation shows that the extent of coronal pulp cavity is easily visible in premolars and molars in panoramic radiograph as shown by Drusini.  However, only second premolars and first molar was chosen for the study; hence, further studies on a larger sample size is required to assess both the first and second, molar and premolars, respectively.
The current literature also endorses gender not being a variable of age in accordance. , A larger sample size is required to rule out the necessity of gender equation as shown by Igbigbi where molar equation was considered more appropriate for males and premolars for female and unknown gender. Also, probable racial variation has to be taken into consideration.
The statistical method uses regression analysis to attain the prediction equation of age using TCI which exhibits the correlation coefficient to be greater (>0.9) in the younger age groups compared to that of older age fragment [Table 6]. The rationale ,, attributed is the complexity of influences governing the anatomic and pathologic parameters affecting the pulp morphology.
The study shows absolute mean error between actual and predicted age for premolars to be 7 months and for molars to be 9 months similar to the study  which was conducted to assess the adult age based on measurement of pulp to tooth area ratio on panoramic radiographs bearing a sensitivity of 91% and specificity of 94.5%.
Thus the coefficients of correlation between actual age and predicted age is established to be significantly immense for first molars and second premolars, similar to those of Ikeda et al. (1985). Drusini (1993) found  a strong correlation with standard errors ranging from 8.79 to 10.08 years. Drusini et al. (1997) found strong correlation of TCI with an error of ±5 years in 81.4% of cases with a standard error of estimate ranging from 5.88 to 6.66 years. A study  using 74 sets of periapical postmortem radiographs found a mean difference between the actual and predicted age to be ±4 years. Another study  evaluated a radiographic method (stereomicroscopic) of age estimation that was statistically comparable to real age of the teeth with a mean error of 0.5-2.5 years and a standard deviation of 4.6-9.8 years. The present article shows, in the vicinity of precision, age judgment comparable to the other studies. ,,,,
The results of the present study are applicable to the limited sample size and similar groups. Future studies are necessary which should include multiple parameters, a larger and diverse sample along with the consideration of physiological and pathological wear and tear relating to diet, habits and culture. Also radiographic parameters, such as magnification has to be taken into account. It is inevitable to avoid image magnification with a digital orthopantomographs (OPG). The authors recommend that it would be prudent to include a correction factor (to compensate for image magnification) with the formula for applying CPI index. The present study devices a simple, practical, non-invasive, cost-effective method for the morphometric analysis of the coronal pulp chamber reduction using TCI on the digital panoramic radiographs. The results of the study should be viewed with caution as the study sample was small. Hence, there is a definite need for similar studies with large south Indian population.
| Conclusion|| |
In conclusion it is noted that the correlation of age with TCI in different age groups is considerable with both the teeth but the difference in correlation between the male and female subjects is trivial. Hence, it may be deduced that TCI is an excellent calculator of age in both the study teeth. The potential of TCI index using digital panoramic radiography could prove useful as a biomarker of aging with increasing availability of digital radiographic systems in the dental institutes and offices. Apparently seeming to be a reliable technique in terms of accuracy it may also make such a practice scientific, widely available, cost-effective and invaluable in fields of dentistry, forensic and anthropology. However, to improve specificity and sensitivity of the research and to achieve as accurate adult age as practically feasible obviates the need for further studies founded on a larger sample size, use of other teeth, involving a larger geographic area and taking into account different environmental factors such as dietary habits, genetic background and history or presence of any illness to know their upshot on the accurateness of age supposition.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]
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