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 Table of Contents  
ORIGINAL RESEARCH
Year : 2021  |  Volume : 13  |  Issue : 4  |  Page : 356-362

Application of modified Kvaal’s age estimation method on extracted maxillary second premolar tooth: An ex-vitro study using cone beam computed tomography


1 Ph. D Scholar, Gujarat University, Ahmedabad, Gujarat, India; Department of Oral Medicine and Radiology, Government Dental College & Hospital, Ahmedabad, Gujarat, India
2 Department of Oral Medicine and Radiology, Government Dental College & Hospital, Ahmedabad, Gujarat, India
3 Department of Oral and Maxillofacial Pathology, Government Dental College & Hospital, Ahmedabad, Gujarat, India

Date of Submission09-Jul-2021
Date of Decision27-Jul-2021
Date of Acceptance30-Jul-2021
Date of Web Publication19-Aug-2021

Correspondence Address:
Dr. Piyush G Limdiwala
Department of Oral Medicine and Radiology, Government Dental College and Hospital, Ahmedabad 380016, Gujarat.
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/JIOH.JIOH_167_21

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  Abstract 

Aim: The aim of this article was to evaluate the applicability of Kvaal et al.’s method using extracted single-rooted maxillary second premolars on cone beam computed tomography (CBCT) sagittal and coronal sections and to develop and validate a new regression equation. Materials and Methods: One hundred and sixty extracted human maxillary second premolars were collected in the age range of 18–62 years, which were extracted on therapeutic ground. The extracted sample teeth were modeled on a customized cast, and CBCT images were acquired. The six variables proposed by Kvaal’s method were measured on CBCT sagittal and coronal sections of the teeth under study. A new regression equation model was formulated and validated on fresh 30 teeth. The data were analyzed using the Statistical Package for Social Sciences (SPSS) software, version 23.0. Results: The mean estimated age using Kvaal’s original formula was 34.38 years (± 12.47) for the overall sample (n = 160). The regression equation based on the data of the maxillary second premolars for CBCT sagittal section was Age (years) = 43.321–5.503 (M)–3.141 (W-L) with a standard error of estimate (SEE) = 12.49; r2 = 0.009 and for CBCT coronal section was Age (years) = 46.482–3.264 (M) +2.639 (W-L) with an SEE = 12.521; r2 = 0.005; P>0.005. The mean estimated age of the test sample (n = 30) was 35.85 ± 12.32 years (P>0.05) in the CBCT sagittal section and 37.87 ± 12.42 years (P>0.05) in the CBCT coronal section by applying the new premolar formula, respectively. Conclusion: There was no significant correlation between the actual age and the estimated age using Kvaal et al.’s method on extracted maxillary second premolar tooth by using CBCT sagittal and coronal sections with the newly developed formula.

Keywords: Age Estimation, CBCT, Extracted Tooth, Kvaal’s Method, Maxillary Second Premolars


How to cite this article:
Limdiwala PG, Shah JS, Pillai JP. Application of modified Kvaal’s age estimation method on extracted maxillary second premolar tooth: An ex-vitro study using cone beam computed tomography. J Int Oral Health 2021;13:356-62

How to cite this URL:
Limdiwala PG, Shah JS, Pillai JP. Application of modified Kvaal’s age estimation method on extracted maxillary second premolar tooth: An ex-vitro study using cone beam computed tomography. J Int Oral Health [serial online] 2021 [cited 2022 Jan 29];13:356-62. Available from: https://www.jioh.org/text.asp?2021/13/4/356/324135




  Introduction Top


Teeth and oral structures play an important role in human identification. Proper identification in forensic odontology is required for ethical, humanitarian, and official records, particularly the legal and criminal investigations.[1] Currently, the need for developing more accurate and non-invasive methods for age estimation, as part of the identification of adult individuals in situations of forensic scenarios, is increasing globally.[2] Generally, radiological, histological, biochemical, and morphological methods were proposed in the literature for dental age estimation. Out of them, histological and biochemical methods have drawbacks such as the requirement of tooth extraction, separation and sectioning, major expense, and advanced laboratory equipment. Over and above, radiological methods have advantages such as simple, faster, economical, and non-invasive reproducible methods that can be employed both on living and unknown dead, either in identification cases or in archaeological investigations.[3]

Age estimation up to puberty can be performed by the development process, dental radiographs (intraoral periapical radiographs, bitewing radiographs, and orthopantomographs), or by a combined radiographic technique of the third molar tooth staging development and hand wrist and cervical vertebrae radiographs. But, after third molar development, it becomes increasingly difficult to assess age accurately. The only aging process and regressive changes of teeth are helpful at adult age.[4] Regressive changes of the tooth have been related to chronological age in adult and subadult populations.[5] As the age advances, the volume of the pulp cavity gradually decreases because of the secondary dentin deposition in the pulp cavity wall.[6] These morphological changes in the pulp cavity serve as one of the most promising predictors for age estimation.

In a review of literature, the method of Kvaal et al. in living adult individuals has been proposed with limitations. However, in many countries, Kvaal et al.’s method was most popularly studied with various radiographic methods, namely conventional intraoral radiographs, digitized intraoral radiographic method, panoramic images, and CBCT, by using different single-rooted teeth (either single or in a combination of maxillary and mandibular teeth). However, being a radiographic method, most of the studies have been conducted on dental X-rays of patients and very few studies had been suggested on extracted teeth.[7]

The aim of our study was to evaluate the applicability of Kvaal et al.’s method using extracted single-rooted maxillary second premolars and to develop and validate a new regression equation.


  Materials and Methods Top


Setting and design

The presented cross-sectional study was undertaken with simple randomly selected samples of 160 extracted human maxillary second premolars. The study was performed at Oral Medicine and Radiology Department of Government Dental College and Hospital, Ahmedabad, Gujarat, India. The extracted teeth were collected irrespective of age and right and left side from patients with greater or equal to the age of 18 years. The samples were collected from patients who have undergone extraction due to periodontal problems, orthodontic purposes, or by any other means.

Sampling criteria

Inclusion criteria were patients’ age greater or equal to 18 years, subjects with valid proof of date of birth, and studied tooth must be clinically free from any developmental, endocrine, or nutritional disorder, which may affect the development of teeth. Only single-rooted maxillary premolars were included in the study. The teeth with restorations, fracture, and decay and root resorption were excluded from the study.

Demographic data collection

One hundred and sixty extracted maxillary second premolars belonged to 85 males (53.12%) and 75 females (46.87%). The mean age of the subjects was 34.38 years. The date of extraction of the tooth was noted down in the study proforma. The chronological age was calculated using the Microsoft Office Excel tool (Microsoft®, Redmond, Washington, USA). The extracted tooth was rinsed with 3% sodium hypochlorite solution followed by 5% normal saline (Otsuka Pharmaceuticals Pvt. Ltd, Ahmedabad, Gujarat, India). The samples were stored in a bottle, labeling the patient data (i.e. Name, date of birth, date of extraction, and reason for extraction).

Study method

For CBCT analysis, an edentulous master cast was prepared with die stone. The six holes were made with bur and carver for retention of the extracted tooth in a ridge area of the mounted cast. The teeth were cleaned and mounted on a master cast [Figure 1]. The master cast was placed at chin rest area of machine, and CBCT scan was obtained with model mode [Figure 2]. CBCT scanning was made using NewTom CBCT machine, [parameters: scanning time: 8 s, 6 mA, 90 kVp, a field of view (FOV): 8*10 mm, 360° rotation, slice thickness 0.3 mm]. All the images were received in DICOM format and analyzed using the NNT software package [NNT version 12.0.1 (2020), Seoul, South Korea].
Figure 1: Extracted maxillary second premolars mounted on a customized cast

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Figure 2: A placement of prepared cast at the chin rest area of CBCT machine and scan was obtained with the model option

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The obtained raw data were prepared with slice sections. Prior to measurement, brightness, contrast, angulation of teeth, and maximum tooth length were achieved in both coronal and sagittal sections.

Measurements

Based on the method proposed by Kvaal et al.,[8] the morphological measurements of the teeth were calculated on the coronal and sagittal sections. Six measurements of each tooth on each CBCT sagittal and coronal section were measured from mesial aspect of tooth: T-maximum tooth length, R-root length on the mesial surface, P-maximum pulp length, A-root and pulp width at the enamel cementum junction (ECJ), B-root and pulp width midway between measurement levels A and C, and C-root and pulp width midway between apex and ECJ variables were measured [Figure 3].
Figure 3: Measurement of morphological parameters, according to Kvaal et al.[8] on sagittal and coronal sectional image

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Statistical analysis

The data were collected in an Excel Spreadsheet and statistical analysis was completed using SPSS software (version 23, SPSS, Inc., Chicago, IL, USA). Descriptive statistics were used to summarize the data (mean, standard deviation, and frequency distributions). The normality test was also tested with the Shapiro–Wilk test. The inter-examiner and intra-examiner variations in the measurement of the variables were checked using the intraclass correlation coefficient (ICC) and the paired t-test statistics. Pearson’s correlation coefficient (r) was calculated to assess the relationship between the dental ratios and age. The ratios were calculated for both coronal and sagittal sections and compared. Linear regression models were built with age designated as the dependent variable. Different age estimation equations were then formulated using different groupings of age.


  Results Top


In the present study, a total of 160 extracted teeth were analyzed for both CBCT sagittal and coronal sectional methods. Out of 160 samples, 85 (53.13%) were males and 75 (46.87%) were females. The data were normally distributed based on the Shapiro–Wilk test. [Table 1] shows the results of inter-examiner and intra-examiner variations analyzed using the ICC and paired t-test. Dahlberg’s statistics were also applied for checking the measurement errors. The results of the above tests showed a good agreement between observers (ICC: 0.78–0.84) in all the measured parameters.
Table 1: Inter-observer and intra-observer agreement for the measured parameters among all methods

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The mean ages were 36.10±12.89 and 32.42±11.76 for males and females, respectively. Out of the 160 teeth samples, 68 (42.5%) were of the maxillary right side and the rest 92 (57.5%) were of maxillary left side teeth. The mean age of total samples was 34.38±12.47 years [Table 2]. For CBCT Kvaal’s method, M (mean values of the ratios) was analyzed as the first predictor and W-L was considered as the second predictor. There was a weak negative correlation between the variables and the actual age in the sagittal section and a weak positive correlation in the coronal section. In the coronal section, only the ‘T’ variable was significantly correlating with the actual age (P = 0.027) [Table 3].
Table 2: Demographic data

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Table 3: Correlation coefficient of variables among the techniques

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The descriptive statistical values and P-values of all methods according to age groups are shown in [Table 4]. The statistically significant value was found in the CBCT sagittal section method for the age group of 18–28.99 years (P = 0.045, P < 0.05 significant). In the age group of 51–61.99 years, CBCT coronal section was found significant, i.e., 0.005 (P < 0.05). There was no significant value found in other age groups for all studied methods. The mean error of ages estimated by various methods according to gender was also tabulated. The statistical significant value was not found much significant in both CBCT section methods.
Table 4: Mean error of ages estimated by CBCT sectional methods according to age and gender groups

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The coefficient of determinants and standard errors of the estimated age for the models were tabulated [Table 5], and we found that the coefficients of determination of the M and W–L variables were found to be low in relation to the chronological age. The standard error of estimate (SEE) for the CBCT sagittal method was found to be 12.49 years and that for the CBCT coronal section was 12.521 years. A linear regression model was analyzed. The coefficient of determination for the CBCT sagittal section method was 0.009 and for the CBCT coronal section method was 0.005. An obtained regression formula for studied maxillary second premolar tooth was not found much significant for both sectional methods (P < 0.05).
Table 5: Regression equation statistics and SEE-compared data with different techniques

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To validate the effectiveness of the developed formula, fresh 30 samples were randomly selected. The mean estimated age of the test sample (n = 30) was 35.85 ± 12.32 years (P>0.05) in the CBCT sagittal section and 37.87 ± 12.42 (P>0.05) in the CBCT coronal section by applying the new premolar formula, respectively, which was not statistically significant.


  Discussion Top


The use of dentition for the assessment of age appears to date back to the early years of the nineteenth century. In 1889, Laccasagne was the first to use changes in the teeth of adults to estimate age.[5] In 1925, Bodecker established that the apposition of secondary dentin correlated with age.[9] In 1995, Kvaal et al. first proposed that the dental radiographs of fully developed teeth can be used as a simple, non-destructive method of age estimation in living individuals and on the unknown dead, either for identification or for archaeological investigations.[8]

As far as concerns with many ethnic populations, the Kvaal method was tested and validated with prescribed linear regression models by using different radiographic techniques. For the Indian population, many studies had been suggested with varied models and formulas. Many authors had supported Kvaal’s method with significant results, and some authors had not, due to insignificant and weak regression model. Still, one can raise concern regarding the reliability of such a method among the same samples, and such data are yet to be compared with the histological method. The present study was proposed on extracted teeth and not in living individuals. None of the studies had ever compared the radiographic techniques on extracted samples. So, this study had been performed on extracted maxillary second premolar tooth samples, for validation of Kvaal’s method on CBCT.

Kvaal’s method had given a formula for six teeth and the present study had a sample only on extracted maxillary second premolars (15/25) irrespective of right and left sides. It was difficult to have all six teeth extracted from the same individual at a time. At an early age, anterior teeth are not advisable for extraction; hence, we were unable to get all six samples. Maxillary second premolars were easily available and advised for orthodontic and periodontal problems. Hence, we had chosen the second premolar in our study.

Kvaal and co-authors developed an age estimation method by using the measurement of six teeth observed on OPG or periapical radiographs. Patil et al.[10] studied Kvaal’s method on the central incisor because it is a single-rooted tooth with the largest pulp area, which is often present in old age. Additionally, angulation errors in radiovisiography are avoided while using central incisors compared with canines and premolars. Anterior teeth show less malalignment and attrition when compared with their opponents, and contain more secondary dentin tissue than other teeth. As we conducted our study on extracted teeth, there were no chances of X-ray angulation errors and maxillary second premolars were the most widely available extracted teeth.

In review of literature, intraoral periapical radiographs had been studied with the same method by Patil et al.,[10] Kvaal et al.,[8] and Sharma and Srivastava,[11] whereas digital OPGs were used by Bosman et al.,[12] Limdiwala and Shah,[6] and Singaraju et al.[13] In all these studies, the authors found significant regression models for all six teeth. But none of the studies had validated the method for individual teeth, especially for maxillary second premolars.

Further looking to inventions in forensic imaging, computed tomography is being most popular in dental practice and provides three-dimensional information about any area of interest in a relatively quick and cost-effective manner.[14] Yolanda et al.[3] used CBCT for Kvaal’s method by keeping in mind that CBCT would be a better method available for the forensic identification of living and cadavers. However, the results were not in agreement. Akay et al.[15] had also tried on 211 single-rooted extracted teeth of the Turkish population. The coefficient of determination was ranged from 0.162 to 0.550, whereas the coefficient of determination was found to be 0.296 and the SEE was found to be 12.75 years in the case of all the teeth. Erbudak et al.[16] found that the pulp width was a better age indicator than the pulp length in the case of digital panoramic radiographic method on a Turkish population. Li et al.[17] had assessed the reliability of Kvaal’s method on canines of 360 northern Chinese individuals by using OPG and suggested that left maxillary canine had stronger correlation with actual age but mandibular canines did not. Moshfeghi et al.[18] studied 150 CBCT scans on living individuals for mandibular canines and they found the regression model to be significant (r2=0.567). In Kvaal’s original study, coefficient of determination (r2) was also good in the case of combined six teeth as well as maxillary second premolar cases (r2 =060).[8] The present study data was also compared with the data of Marroquin Penaloza. The authors had applied Kvaal’s method on 101 CBCT of Malaysian populations. The study was performed in the same sagittal and coronal view of maxillary central and laterals but not in maxillary second premolars. They documented that Kvaal’s method was much accurate on panoramic radiographs (SEE ±10.02 years) while comparing result with CBCT results (SEE ±10.58 years).[14]

In the present study, the regression equation model was not significant in case of both CBCT sectional methods. This may be due to the poor correlation coefficient relationship among all measured ratios [Table 2], except only T ratio was significant in the case of the coronal section method. Further, the r2 value was 0.009 (CBCT sagittal method) and the coronal section method had r2 value of 0.005. In both the methods, the coefficient of determinant was merely similar and not accordance with Kvaal’s original study.[8]

In reason to this, odontometric data of CBCT for sagittal and coronal measurements did not change the final outcome. Also, CBCT technique is much sensitive and subjective while choosing the sections to be studied. The need to properly align the teeth in the sagittal and coronal plane demands more time and accuracy and reproducibility than the traditional Kvaal et al.’s approach for intraoral and panoramic radiographs. In this way, CBCT cannot be exempted for such image acquisition-related intrinsic artifacts and thus measurements. This factor may explain the reason for difference in SEE and actual age and the poor accuracy of such methods based on measurements.[14] The present study was performed on extracted tooth and not in living individuals; however, till date, many kinds of literatures had been published for combination of teeth in vivo design and not in vitro design using individual tooth for dental age assessment while validating Kvaal’s method.

There is no question that radiological method for dental age assessment is a reliable tool. This study had compared new concept of two CBCT sectional methods on extracted tooth to use and to propose new method, but it is unfortunate that we could not find a good co-relation between chronological age and calculated age in any case. There have been various studies of comparison of Kvaal’s method with other studies like Camiere’s method. Based on the results, we suggest that the study should be conducted on a larger scale for more accurate and reliable results, such that a formula be derived that can be used in the Indian population with greater accuracy. In our study, we only studied maxillary second premolars. However, as the maxillary second premolars are more prone to decay, and less likely to be preserved in the long term, it would be suggested that other single-rooted teeth should be considered as well, maybe incisors and canines, if one would go for to establish the technique on an extracted tooth that would predict the dental age better in specific populations and ethnicity, especially when cadaveric samples are challenged.


  Conclusion Top


CBCT sagittal and coronal sectional radiographic methods had a very low coefficient of determinant while applying Kvaal’s method on extracted maxillary second premolar. There was no significant correlation found between the actual age and the estimated age with the new premolar formula. The method has limitations to apply on extracted single-rooted maxillary second premolars. A modification of such technique is required and should be studied on a large scale in a given population or else one can choose other volume calculation-based methods.

Acknowledgements

The authors wish to acknowledge the support of the staffs and Interns of Oral and Maxillofacial Surgery Department during the sample collection.

Financial support and sponsorship

Nil.

Conflict of interest

There was no conflict of interest in this study.

Author contributions

P. G. L.: Concept and design, experimental studies, data acquisition; definition of intellectual content, literature search, data acquisition, manuscript editing, writing and guarantor; clinical studies, data analysis, and manuscript preparation. J. S. S.: concept and design, experimental studies, definition of intellectual content, literature search, manuscript editing, and review. J. P. P.: data acquisition, statistical test and result drafting, manuscript editing, and review. All authors have read and approved the manuscript. The authors agree to copyright transfer in case of manuscript acceptance for publication.

Ethical policy and Institutional Review Board statement

The study constituted based on approval from the Institutional Ethical Committee of GDCH (IECGDCH/5.1/2015, dated 03/02/2016).

Declaration of patient consent

The patient informed written consent for extraction was obtained from each patient along with their demographic data such as date of birth and gender.

Data availability statement

The data set used in the current study is available on valid request by contacting corresponding author mail.



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

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



 

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