Modified Anderson’s Method Age Estimation Using Mandibular Left and Right Third Molar in South Indian Population

Yoshita Guntupalli and Abirami Arthanari^*

^*Correspondence: Abirami Arthanari, Department of Forensic Odontology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Science, Saveetha University, Chennai, India, Email:

Author info »

Introduction

With the ever-increasing crime rate in our society, the field of forensic sciences has become highly evolved. Forensic dentists play a pivotal role in various areas of crime scene investigations and thereby help solve innumerable mysteries. Teeth appear to be vital pieces of evidence in several such investigations. Teeth are preserved in the closed cavities of the mouth and are generally resistant to the threatening environmental conditions that may be associated with the death of an individual, making them very useful in postmortem analysis. Teeth thus obtained may be useful in age estimation of the deceased victim or in determining his blood group [1].

The need to rely on proper, simple, and accurate methods for age estimation in adults is still a world-wide issue. It has been well documented that teeth are more resistant than bones to the taphonomic processes, and that the use of methods for age estimation based on dental imaging assessment are not only less invasive than those based on osseous analysis, but also have shown similar or superior accuracy in adults [1,2].

While age estimation of unidentified corpses and skeletons for identification purposes has a long tradition in forensic sciences, age estimation of living persons has formed a relatively recent area of forensic research which is becoming increasingly important. The international interdisciplinary Study Group on Forensic Age Diagnostics (AGFAD) issued recommendations for age estimation of living persons for the purpose of criminal, civil, asylum and old-age pension procedures as well as for determining the sex and age of skeletons [3].

Teeth present with peculiar and comparable features of age-associated regressive changes along with dental procedures, which make them a mirror reflection of age changes from cradle to the grave of an individual. Age estimation in adults poses an enigma to forensic dentists because as the age advances, the dentitions get influenced by numerous exogenous and endogenous factors which may lead to discrepancies between dental age and chronological age [4].

Coming to the methods of age estimation, Demirjian's method is one of the most commonly used methods to evaluate dental age and has been widely used in many countries. It is found that compared with the chronological ages, most of the estimated ages are overestimated. By combining research results of many scholars and by analyzing, it can be assumed that this situation may be related with race, region, sex, etc. [5].

In a substantial re-working of Gustafson's data, Maples and Rice corrected Gustafson's regression statistics and found that the error associated with the age estimate was nearly double that claimed by Gustafson [6].

In our study we have taken up modified Anderson’s method for age estimation. In this method we estimate the chronological age of teeth based on the developmental stage of teeth. The advantage of this system is that, being based on all deciduous and permanent teeth, it is far more versatile than many derived in the previous studies. This versatility lies in the fact that any teeth can be used in the assessment. This can be very useful from a clinical point of view in forensic cases when fragmentation, decomposition or predation of the remains may mean that not all teeth are actually recovered [7].

Many studies have been conducted previously to determine age using different methods of age estimation with teeth. Our team has extensive knowledge and research experience that has translate into high quality publications [8-27]. The aim of our study is to use the modified Anderson’s method to evaluate the accuracy of age estimation.

Materials and Methods

100 OPGs, 50 male and 50 female from the age group 10- 20 were taken for age estimation. The population taken for the study was from South India. They were divided into the following groups according to age - 10-10.9, 11-11.9, 12-12.9, 13-13.9, 14-14.9, 15-15.9, 16-16.9, 17-17.9, 18- 18.9, 19-19.9 9. Table 1 represents the age distribution of the samples tested. The teeth taken for the study were 38 and 48. They were compared to Anderson's chart given below Table 2. Anderson's chart consists of tooth staging based on the developmental stages of the teeth. They were staged using the multirooted stages by comparing the image in the chart with the OPG image. Figure 1 shows a sample OPG. Data was tabulated in excel sheet and then transferred to SPSS for statistical analysis. The mean, standard deviation, standard error mean and p value were determined and value less than 0.05 was considered to be statistically significant.

Table 1: Sample distribution.

Table 2: Staging of teeth according to modified anderson’s method.

Figure 1: OPG with chronological age 17.9 years, showing staging of 38 as 12 and staging of 48 as 13.

Results

The mean chronological age of 50 male samples was found to be 14.84. The mean estimated age of 50 male samples for 38 was found to be 7.34 (Table 3) and for 48 was found to be 7.40 (Table 4). The mean chronological age of 50 female samples was found to be 14.81. The mean estimated age of 50 female samples for 38 was found to be 6.64 (Table 3) and for 48 was found to be 6.72 (Table 4). Upon comparison of tooth staging of 38 in males and females, p value was found to be 0.015 (<0.05) and was hence statistically significant (Table 5). Similarly on comparing tooth staging of 48 in males and females p=0.552 (>0.05) which was not statistically significant (Table 5).

Table 3: Standard deviation and standard deviation error for tooth staging 38 in males and females. For 38 male, mean was found to be 7.34, standard deviation was found to be 3.526 and standard deviation error was found to be 0.499. For 38 females, mean was found to be 6.64, standard deviation was found to be 3.646 and standard deviation error was found to be 0.516.

In the present study, it was observed that there is statistical significance upon comparison of tooth staging of 38 in males and females while there is no statistical significance between 48 of males and females. The standard deviation for male 38 staging is 3.526 (Table 3) while that for females is 3.646 (Table 3). The standard deviation for male 48 staging is 3.620 (Table 4) and that for females is 3.603 (Table 4). The mean of estimated age for 38 in males was found to be 7.34 (Table 3) while that of females for 38 was found to be 6.64 (Table 3). Similarly the mean estimated age for 48 in males was found to be 7.40 (Table 4) and that for females was found to be 6.72 (Table 4).

Table 4: Standard deviation and standard deviation error for tooth staging 48 in males and females. For 48 in males, mean was found to be 7.40, standard deviation was found to be 3.620 and standard deviation error was found to be 0.512. For 48 in females, mean was found to be 6.72, standard deviation was found to be 3.603 and standard deviation error was found to be 0.510.

Figure 2 shows tooth staging of 38 males vs. the percentage of abundance of samples in each stage and it was found that most abundance was seen in 10th tooth stage with 20%. Figure 3 shows the tooth staging of 48 males vs the percentage of abundance of samples in each stage. Most abundance was found in tooth stage 10 with 18% of samples. Similarly Figure 4 shows the tooth staging of 38 females vs the percentage of abundance of samples in each stage and it was found that most abundance was found in tooth stage 4 with 24% of total samples. Figure 5 shows the tooth staging of 48 females vs. the percentage of abundance of samples in each stage.

Figure 2:Tooth staging of 38 males vs the percentage of males with 38 in that stage. X axis represents the tooth staging number of 38 while the Y axis represents the percentage of males. Highest prevalence was found in stage 10.

Figure 3:Graph showing tooth staging of 48 males vs. the percentage of males with 48 in that stage. X axis represents the tooth staging number of 48 while the Y axis represents the percentage of males. Highest prevalence was found in stage 10.

Figure 4:Graph showing tooth staging of 38 females vs. the percentage of females with 38 in that stage. X axis represents the tooth staging number of 38 while the Y axis represents the percentage of females. Highest prevalence was found in stage 4.

Figure 5:Graph showing tooth staging of 48 females vs. the percentage of females with 48 in that stage. X axis represents the tooth staging number of 48 while the Y axis represents the percentage of females. Highest prevalence was found in stage 4.

We have taken ages 12-12.9, 14-14.9 and 18-18.9 for comparison between males and females in the graphs as these ages are the legally approved standard for comparison, from Figure 6 and Figure 7 we find that not much variation is seen between males and females in ages 12-12.9 and 14-14.9, however in the age group 18-18.9, dental maturity in males was found to be significantly higher in males than females for both 38 and 48.

Figure 6:Bar Graph depicts the association between the chronological age of males and females and the mean of tooth staging of 38 males and females. X axis represents the chronological age of the samples and Y axis represents the mean of tooth staging of 38. Blue denotes tooth staging of 38 males and green denotes tooth staging of 38 females. Not much variation was seen between males and females in the groups 12-12.9 and 14-14.9. However males were found to have higher dental maturity in the age group 18-18.9 when compared to females. The difference was statistically significant (Chi-Square test; p-value = 0.015-significant).

Figure 7:Bar Graph depicts the association between the chronological age of males and females and the mean of tooth staging of 48 males and females. X axis represents the chronological age of the samples and Y axis represents the mean of tooth staging of 48. Blue denotes tooth staging of 48 males and green denotes tooth staging of 48 females. Not much variation was seen between males and females in the groups 12-12.9 and 14-14.9. However males were found to have higher dental maturity in the age group 18-18.9 when compared to females. The difference was however not statistically significant (Chi-Square test; p-value = 0.552-not significant).

Table 5: P values for tooth staging 38 in males and females and tooth staging 48 in males and females. P value for 38 showed statistical significance (p=0.015<0.05) while that for 48 was not statistically significant (p=0.552>0.05).

Discussion

In a recent study conducted by Abirami et al, 2020 to estimate the age of second and third molars by modified Gleiser and Hunt method, it was found that there was a difference between Male and Female root maturation in relation to 38 and combination of 37 & 38 (nearly 1.2yrs variation). So, males are showing more accuracy than females in root maturation. The Standard deviation for Male 37 staging was ± 2.15years and for 38 staging was ± 1.29 years. And, the Standard deviation for Female 37 staging was ± 2.58 years and in 38 staging was ± 2.24 years [28]. According to Jain, et al. [29] although the mineralization stages of the teeth indicated physiologic development more than chronological age, the dental mineralization stages are closely related to chronological age.

In a study conducted by Maber et al, 2006 conducted to determine the accuracy of age estimation using teeth it was found that Demirjian overestimated age, while Nolla, et al. methods under-estimated age . For individual teeth using Haavikko's method, the first premolar and second molar were most accurate; and more accurate than the mean value of all developing teeth [30].

In a recent study conducted by Cameriere, et al. using Cameriere method, the method yielded a mean prediction error of 0.407 years for girls and 0.380 years for boys. Although the accuracy of this method was better for boys than for girls, the difference between the two mean prediction errors was not statistically significant [31]. In another study conducted by Fernandes et al, 2011 to determine the accuracy of the Cameriere method there was no statistically significant difference between chronological and estimated ages on considering boys and girls separately. However, on analyzing each age group, the estimated age was significantly higher than chronological age from 5 to 10 years old and significantly lowers from 11 to 14 years old [32].

Developing teeth are commonly the criteria used for age estimation in children and young adults. The limitations of our study are that we have taken only 100 samples, i.e., the sample size is limited. Coming to the future scope of our study, it helps us determine the accuracy of age estimation of the modified Anderson’s method.

Conclusion

The modified Anderson’s method of age estimation showed significance for tooth staging of 38 between males and females. Overall it slightly underestimated the ages of the participants. However with greater sampling size and further research, development of a more accurate formula can be done for efficient age estimation of OPG samples.

Acknowledgement

We thank Saveetha Dental College and Hospitals for providing us the support to conduct the study.

Conflict of Interest

The author declares that there was no conflict of interest in the present study.

Source of Funding

The present study was funded by the following agencies

• Saveetha Institute of Medical and Technical Sciences.

• Sarkav Health Services.

References

1. Shah P, Velani PR, Lakade L, et al. Teeth in forensics: A review. Indian J Dent Res 2019; 30:291–299.

Indexed at, Google Scholar, Cross Ref

2. Marroquin TY, Karkhanis S, Kvaal SI, et al Age estimation in adults by dental imaging assessment systematic review. Forensic Sci Int Genet 2017; 275:203–211.

Indexed at, Google Scholar, Cross Ref

3. Schmeling A, Geserick G, Reisinger W, et al. Age estimation. Forensic Sci Int 2007; 165:178-181.

 Google Scholar, Cross Ref

4. Verma M, Verma N, Sharma R, et al. Dental age estimation methods in adult dentitions: An overview. J Forensic Dent Sci 2019; 11:57–63.

Indexed at, Google Scholar, Cross Ref

5. Han MQ, Jia SX, Wang CX, et al. Accuracy of the Demirjian, Willems and nolla methods for dental age estimation in a northern Chinese population. Arch Oral Biol 2020; 118:104875.

Indexed at, Google Scholar, Cross Ref

6. Lucy D, Pollard AM. Further comments on the estimation of error associated with the Gustafson dental age estimation method. J Forensic Sci 1995; 40:222-227.

Indexed at, Google Scholar, Cross Ref

7. Cherian JM, Thomas AM, Kapoor S, et al. Dental age estimation using Willems method: A cross-sectional study on children in a North Indian city. J Oral Maxillofac Pathol 2020; 24:383.

Indexed at, Google Scholar, Cross Ref

8. Princeton B, Santhakumar P, Prathap L. Awareness on preventive measures taken by health care professionals attending COVID-19 patients among dental students. Eur J Dent 2020; 14:105–109.

Indexed at, Google Scholar, Cross Ref

9. Mathew MG, Samuel SR, Soni AJ, et al. Evaluation of adhesion of Streptococcus mutans, plaque accumulation on zirconia and stainless steel crowns, and surrounding gingival inflammation in primary molars: randomized controlled trial. Clin Oral Investig 2020; 24:3275–3280.

Indexed at, Google Scholar, Cross Ref

10. Sridharan G, Ramani P, Patankar S, et al. Evaluation of salivary metabolomics in oral leukoplakia and oral squamous cell carcinoma. J Oral Pathol Med 2019; 48:299–306.

Indexed at, Google Scholar, Cross Ref

11. Hannah R, Ramani P, Ramanathan A, et al. CYP2 C9 polymorphism among patients with oral squamous cell carcinoma and its role in altering the metabolism of benzo [a] pyrene. Oral Surg Oral Med Oral Pathol Oral Radiol 2020; 130:306-312.

Indexed at, Google Scholar, Cross Ref

12. Antony JVM, Ramani P, Ramasubramanian A, et al. Particle size penetration rate and effects of smoke and smokeless tobacco products - An invitro analysis. Heliyon 2021; 7:e06455.

Indexed at, Google Scholar, Cross Ref

13. Sarode SC, Gondivkar S, Sarode GS, et al. Hybrid oral potentially malignant disorder: A neglected fact in oral submucous fibrosis. Oral Oncol 2021; 105390.

Indexed at, Google Scholar, Cross Ref

14. Ramani P, Tilakaratne WM, Sukumaran G, et al. Critical appraisal of different triggering pathways for the pathobiology of pemphigus vulgaris-A review. Oral Dis 2021.

Indexed at, Google Scholar, Cross Ref

15. Chandrasekar R, Chandrasekhar S, Sundari KKS, et al. Development and validation of a formula for objective assessment of cervical vertebral bone age. Prog Orthod 2020; 21:38.

Indexed at, Google Scholar, Cross Ref

16. Subramanyam D, Gurunathan D, Gaayathri R, et al. Comparative evaluation of salivary malondialdehyde levels as a marker of lipid peroxidation in early childhood caries. Eur J Dent 2018; 12:67–70.

Indexed at, Google Scholar, Cross Ref

17. Jeevanandan G, Thomas E. Volumetric analysis of hand, reciprocating and rotary instrumentation techniques in primary molars using spiral computed tomography: An in vitro comparative study. Eur J Dent 2018; 12:21–26.

Indexed at, Google Scholar, Cross Ref

18. Ponnulakshmi R, Shyamaladevi B, Vijayalakshmi P, et al. In silico and in vivo analysis to identify the antidiabetic activity of beta sitosterol in adipose tissue of high fat diet and sucrose induced type-2 diabetic experimental rats. Toxicol Mech Methods 2019; 29:276–290.

Indexed at, Google Scholar, Cross Ref

19. Sundaram R, Nandhakumar E, Haseena Banu H. Hesperidin, a citrus flavonoid ameliorates hyperglycemia by regulating key enzymes of carbohydrate metabolism in streptozotocin-induced diabetic rats. Toxicol Mech Methods 2019; 29:644–653.

Indexed at, Google Scholar, Cross Ref

20. Alsawalha M, Rao CV, Al-Subaie AM, et al. Novel mathematical modelling of Saudi Arabian natural diatomite clay. Mater Res Express 2019; 6:105531.

Indexed at, Google Scholar

21. Yu J, Li M, Zhan D, et al. Inhibitory effects of triterpenoid betulin on inflammatory mediators inducible nitric oxide synthase, cyclooxygenase-2, tumor necrosis factor-alpha, interleukin-6, and proliferating cell nuclear antigen in 1, 2-dimethylhydrazine-induced rat colon carcinogenesis. Pharmacogn Mag 2020; 16:836.

Indexed at, Google Scholar

22. Shree KH, Hema Shree K, Ramani P, et al. Saliva as a diagnostic tool in oral squamous cell carcinoma–A systematic review with meta analysis. Pathol Oncol Res 2019; 25:447–453.

Indexed at, Google Scholar, Cross Ref

23. Zafar A, Sherlin HJ, Jayaraj G, et al. Diagnostic utility of touch imprint cytology for intraoperative assessment of surgical margins and sentinel lymph nodes in oral squamous cell carcinoma patients using four different cytological stains. Diagn Cytopathol 2020; 48:101–110.

Indexed at, Google Scholar, Cross Ref

24. Karunagaran M, Murali P, Palaniappan V, et al. Expression and distribution pattern of podoplanin in oral submucous fibrosis with varying degrees of dysplasia–An immunohistochemical study. J Histotechnol 2019; 42:80–86.

Indexed at, Google Scholar, Cross Ref

25. Sarode SC, Gondivkar S, Gadbail A, et al. Oral submucous fibrosis and heterogeneity in outcome measures: A critical viewpoint. Future Oncol 2021; 17:2123–2126.

Indexed at, Google Scholar, Cross Ref

26. Raj Preeth D, Saravanan S, Shairam M, et al. Bioactive Zinc(II) complex incorporated PCL/gelatin electrospun nanofiber enhanced bone tissue regeneration. Eur J Pharm Sci 2021; 160:105768.

Indexed at, Cross Ref

27. Prithiviraj N, Yang GE, Thangavelu L, et al. Anticancer compounds from starfish regenerating tissues and their antioxidant properties on human oral epidermoid carcinoma kb cells. In: Pancreas. Lippincott Williams and Wilkins Two Commerce SQ, 2001.
28. Arthanari A, Doggalli N, Vidhya A, et al. Age estimation from second & third molar by modified gleiser and hunt method: A retrospective study. Indian J Forensic Med Toxicol 2020; 14.

Indexed at, Google Scholar

29. Jain V, Chowdhry A, Sircar K, et al. Application of comprehensive chart for dental age estimation (DAEcc) based on demirjian method using orthopantograms: A pilot study. Forensic Sci Int 2019; 1:100017.

Indexed at, Google Scholar, Cross Ref

30. Maber M, Liversidge HM, Hector MP. Accuracy of age estimation of radiographic methods using developing teeth. Forensic Sci Int 2006; 159: 68–73.

Indexed at, Google Scholar, Cross Ref

31. Cameriere R, Ferrante L, Liversidge HM, et al. Accuracy of age estimation in children using radiograph of developing teeth. Forensic Sci Int 2008; 176:173–177.

Indexed at, Google Scholar, Cross Ref 

                  
32. Fernandes MM, Tinoco RL, de Braganca DP, et al. Age estimation by measurements of developing teeth: accuracy of Cameriere’s method on a Brazilian sample. J Forensic Sci 2011; 56:1616-1619.

Indexed at, Google Scholar, Cross Ref