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 Table of Contents  
Year : 2021  |  Volume : 33  |  Issue : 3  |  Page : 306-313

Application and validation of Lamendin et al.'s adult age estimation method using mandibular premolar teeth on Western Indian (Gujarati) population: An experimental study

1 Ph.D. Scholar, Gujarat University; Department of Oral Medicine and Radiology, Government Dental College and Hospital, Ahmedabad, Gujarat, India
2 Scientific Officer, Sherlock Institute of Forensic Science, Delhi, India
3 Department of Oral Medicine and Radiology, Government Dental College and Hospital, Ahmedabad, Gujarat, India
4 Department of Oral and Maxillofacial Pathology, Government Dental College and Hospital, Ahmedabad, Gujarat, India

Date of Submission01-Mar-2021
Date of Decision15-May-2021
Date of Acceptance13-Jul-2021
Date of Web Publication28-Sep-2021

Correspondence Address:
Dr. Piyush G Limdiwala
Department of Oral Medicine and Radiology, Government Dental College and Hospital, Ahmedabad - 380 016, Gujarat
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jiaomr.jiaomr_33_21

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Aim: To evaluate the applicability of the method by Lamendin et al. for adult dental age estimation using extracted single-rooted mandibular first premolar in the Western Indian population and to develop a new regression equation. Materials and Methods: Ninety-seven extracted mandibular first premolars were collected from patients in the age range of 15–75 years. The root height, root translucency, and periodontosis parameters were measured. Lamendin's formula was tested on 77 randomly selected specimens. The regression formula was applied to the remaining 20 specimens to validate the same. The data were analyzed using the Interclass Correlation Coefficient (ICC), paired t-tests, and Pearson's correlation tests. Results: The mean estimated age using Lamendin's original formula was 44.75 years (±10.52) for the overall sample (N = 77). There was an overestimation of the age of the study sample by 0.11 year (SD = 11.14; df = 76; P > 0.05). The regression equation based on the data of the mandibular first premolar was Age (Yrs.) =18.423 + 0.441 (P) +0.423 (T) with a SEE = 11.07; r2 = 0.540; Sig. P <0.001. The mean estimated age of the test sample (N = 20) was 35.85 years ± 7.32 (P < 0.05) and 37.87 ± 6.19 (P > 0.05) by applying the new premolar formula and Lamendin's original formula, respectively. Conclusion: A strong positive correlation was found between the actual age and the estimated age using Lamendin's method, and there was no significant difference between the actual age and the estimated age of the study population using Lamendin's original and new premolar formula.

Keywords: Age estimation, Lamendin's method, mandibular premolars, periodontosis, root height, root translucency

How to cite this article:
Limdiwala PG, Sugandha N, Shah JS, Pillai JP. Application and validation of Lamendin et al.'s adult age estimation method using mandibular premolar teeth on Western Indian (Gujarati) population: An experimental study. J Indian Acad Oral Med Radiol 2021;33:306-13

How to cite this URL:
Limdiwala PG, Sugandha N, Shah JS, Pillai JP. Application and validation of Lamendin et al.'s adult age estimation method using mandibular premolar teeth on Western Indian (Gujarati) population: An experimental study. J Indian Acad Oral Med Radiol [serial online] 2021 [cited 2022 Dec 3];33:306-13. Available from: http://www.jiaomr.in/text.asp?2021/33/3/306/326884

   Introduction Top

The tooth is one of the important biological markers of aging because of its highly mineralized content and its ability to withstand postmortem changes. The dental maturity status is considered to be a reliable parameter for assessing the biological age for criminal, forensic, and anthropological purposes. In forensic human identification, age estimation is very important, especially where there is no information related to the deceased. From the forensic human identification perspective, the dental age estimation is done to roughly estimate the age at the time of death.[1] It narrows down the search of missing persons and aids in the identification process of the unidentified deceased. Thus, dental age estimation can be considered as one of the core domains of forensic odontology. The dental age estimation process mostly depends on the mineralization stages of the developing tooth, its eruption status, and its post-formative morphological changes.[2],[3],[4],[5],[6],[7] The post-developmental changes in a tooth are evident in the morphology as gross anatomical changes, such as attrition of cusps, periodontal attachments level, and root resorption.[7],[8] Age estimation methods are applying these parameters, with the drawback of sacrificing the tooth under query.[8],[9],[10]

As the post-development changes in the teeth also contribute as parameters for assessing age, these methods are applied as adult age estimation techniques. Gustafson was the first to estimate the age based on six characteristics of the dental structure: attrition, periodontosis, secondary dentine, cementum apposition, apical resorption, and translucency tooth root.[7] Later with modifications, Lamendin et al. published a technique of age estimation based on the translucency of the tooth root and periodontosis alone.[11] Unlike the earlier Gustafson's method, Lamendin et al. used only three criteria on the labial surface of intact incisor tooth without any need for the sectioning of the query tooth. The dentinal translucency, also known as sclerosis of root dentine, is the distance from the root apex to the maximum height of transparency along the root surface. It results from the deposition of hydroxyapatite crystals in the dentinal tubule.[12] When the dentinal tubules are occluded, then the refractive indices are equalized. That is the reason for the dentine to become transparent.[13] The translucency shows a significant relationship with age.[14] Periodontosis is produced by the degeneration of soft tissue around the tooth from the cementoenamel junction to the root apex. This is evident as a yellow dark line on the surface of the root.[15] The root length is measured from the cervical line to the root end. [Figure 1]. Based on the above-measured parameters, Lamendin et al. gave the following age estimation regression formula:
Figure 1: Schematic showing the measured parameters in the mandibular premolar

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Age = (0.18 × P) + (0.42 × T) +25.53,

where P and T are defined as follows:

P = (measured periodontal recession height × 100)/measured root height

T = (measured root transparency height × 100)/measured root height.

This formula is based on the data obtained from the single-rooted maxillary central incisor tooth of a French population belonging to European ancestry. Despite criticisms, Lamendin's method has been applied and tested in other populations by several researchers.[16],[17],[18],[19],[20],[21],[22],[23] Though Lamendin's method is recommended as one of the adult age estimation techniques by the American Board of Forensic odontology dental age estimation committee, studies validating Lamendin's method in the Indian population are lacking.[1] This is a simple method using an intact extracted tooth and considering the number of extractions being performed at the institutional level in India and by applying the odontometrics, the authors foresee the feasibility of utilizing at least the single-rooted teeth for validating the Lamendin's age estimation method in the Indian population.[24],[25] In many real-life forensic cases involving human skeletal remains, the second and the third co-authors of this study have been asked by the forensic authorities to estimate the age at the time of death based on some avulsed teeth from the crime scene, especially the single-rooted premolars.

Currently, there is no validated visual and non-radiographic adult age estimation technique from intact tooth, especially the mandibular premolars for the Indian population. Hence, the present study was designed to apply Lamendin's formula on extracted single-rooted mandibular premolars and to derive a new regression equation for age estimation. The author also aimed at validating the modified formula using a separate set of test samples from the same population. The null hypothesis in this study stated that there was no significant difference in the actual age and the estimated age of the study sample using Lamendin's formula.

   Materials and Methods Top

Setting and design

This is a retrospective, cross-sectional, observational study undertaken with a randomly selected sample of 97 previously extracted mandibular first premolars with a single root. The extracted teeth belonged to patients reported during the pre-COVID period who were in the age range of 15–75 years. The actual calendric age was calculated from the date of birth and the date of extraction of the tooth using Microsoft Excel. The sample size was determined using the following formula:

Sample analysis

Sample size (n) = (Z) 2× (σ) 2/d2, where Z is the standard value for the corresponding level of confidence (at 95%, the value is 1.96), σ is the standard deviation based on the earlier study, and d is the margin error standard error and the standard deviations of a previous study with a 95% level of confidence.[26] The mean calendric age (CA) of study subjects was 44.64 years (±16.10). The samples were grouped according to the age range. The distribution of the sample according to the age group is shown in [Figure 2]. The teeth were extracted for therapeutic reasons (orthodontic and periodontal disease) in the oral surgery department of the institute. The periodontally affected teeth were mobile in their respective socket, and the extraction of such teeth was undertaken as the patients were not willing for any periodontal therapy and gave consents for extraction. Those periodontally weaker teeth that were extracted were not showing any signs of other periodontal pathologies such as hypercementosis, root resorption, or any periodontal abscess or cyst. Those teeth with clinical evidence of cervical caries having endodontic fillings and conservative dental restorations were also excluded from the study. The extraction was done as per the institutional standard surgical protocols, and proper care was taken to preserve the anatomic details of the tooth during the procedure.
Figure 2: Pie chart showing the frequency distribution of the sample according to the age range

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Ethical approval and consent to participate

Ethical approval was obtained from Institutional Ethics Committee, GDCH (IECGDCH/OMR.14/2019, 10/4/2019). The study criteria were developed as per the Declaration of Helsinki (2014) ethical principles. Informed written consent for participation in the study and the consent to publish the research data were obtained from the willing participants. In the case of minors (below 18 years), written consents were obtained from a parent or legal guardian of participants.

Sampling criteria

The inclusion criteria included only single-rooted first premolars with clearly defined cemento-enamel junction and clearly visible epithelial attachment mark on the root surface. Those teeth with signs of caries, root fracture, resorption, periapical pathology, and/or signs of destruction in the cervical area like abrasion, erosion and abfraction were excluded from the analysis. The date of extraction of the tooth was noted down in the study proforma. The chronological age was calculated using the “DATEDIF” function in the Microsoft Office Excel® 2007 (Microsoft®, Redmond, Washington, U. S). Each tooth was obtained from a single extraction case and thus each tooth belonged to different individuals. Seventy-seven of these 97 teeth (79.4%) were randomly selected for the application of the Lamendin's original age estimation formula and to derive a new regression equation.

Study method and observational parameters

Each tooth was given a code number to preserve anonymity. The study hypothesis (null hypothesis) was framed in such a way that the mean estimated age does not significantly differ from the mean actual age of the study samples. To apply the Lamendin method, the following three measurements were recorded with a digital vernier caliper (Mitutoyo®, Digimatic, Mitutoyo Corp., Japan) accurate to 0.01 mm: Root height, Periodontosis, and Root translucency.

The root height and the periodontosis were measured on the labial surfaces and the root translucency was measured from the proximal surface of each premolar. Root height was taken as the distance between the apex of the root and the cemento-enamel junction of the premolars. The periodontal height (periodontosis) was measured as the maximum distance from the cemento-enamel junction to the line left by the soft tissue attachment on the neck and/or root of a tooth. Root transparency was observed by viewing the tooth against a bright light source, LED RC torch (Bajaj® LED RC power consumption torch 1.5 W and Lantern 1.2 W with a power of battery 3.7 V, 1200 mAh) in a dark room and measuring from the apex of the root to the maximum height of visible transparency within the root.

To avoid potential bias, all the measurements were taken under the blinded condition and on two different occasions spanning 15 days by the same observer (SN). All the measurements were taken to the nearest millimeter. To determine the reliability and the repeatability of the measurements, 25 teeth from the set of 77 teeth were randomly selected and all the three variables were re-measured by another observer (JP). The quantitative data was entered in the Office Excel® 2007 (Microsoft®, Redmond, Washington, U.S) for further analysis. The measured parameters were applied to the Lamendin's formula to estimate the age. A new regression formula was derived based on the available data set. The second set of 20 mandibular premolars from patients with a mean age of 29.98 years (±16.63) was used to statistically validate the newly derived formula.

Data analysis

The data were analyzed using the Statistical Package for Social Sciences (SPSS) software (SPSS for Windows, V.16.0. SPSS Inc., Chicago, IL, USA; now IBM Corp., Armonk, USA). A P value of < 0.05 was set as statistically significant. The Intraclass Correlation Coefficient (ICC) and paired t-tests were applied to test the reliability and repeatability of the measured data. Pearson's correlation coefficient statistics were applied to identify the degree or strength of the linear relationship between the actual age and the age changes parameters like translucency and periodontosis and with the estimated age. The regression models for estimating the age using periodontosis and translucency as variables were formulated for the mandibular premolars. The paired t-test was applied to test the difference in the actual age and the estimated age using the new formula and Lamendin's formula.

   Results Top

The study included 77 extracted single-rooted mandibular premolars from individuals in the age range of 15–75 years, with a mean age of 44.64 years (±16.10). There was a strong inter-observer agreement, and no significant difference in the measured variables between the two observers [Table 1]. [Table 2] shows the descriptive statistics of the measured and calculated parameters. There was a significant positive correlation between the age and the age changes parameters such as root transparency (r = 0.715, P < 0.001) and periodontosis (r = 0.502, P < 0.001). However, root length showed a weak insignificant correlation with age [Table 3]. The age estimated using Lamendin's original formula showed a significant correlation with the actual age (r = 0.725, P < 0.001) [Figure 3]. The mean estimated age was 44.75 years (±10.52) for the overall sample. Thus, Lamendin's formula overestimated the actual age of the study sample by 0.11 years (SD = 11.14; df = 76; P > 0.05). Hence, the null hypothesis was accepted. From the scatter plot [Figure 4], it is evident that nearly 50% of the study samples were underestimated and 97.4% of them were above 40 years. In addition, nearly 31% of the overestimated cases were above 40 years. There was a significant difference in the mean difference between CA and EA among the age groups [Table 4]. However, between groups 2 and 3, the difference was not significant. The maximum difference was observed in the age group of 60–75 years. The formula underestimated the age in the male samples (mean = −1.99 years) and overestimated in the female samples (mean = 2.51 years) However, there was no significant gender difference in the measured and estimated values [Table 5]. Hence, the regression equations for estimating the age using the root translucency and periodontosis parameters were derived for the overall sample. The regression equation based on the data of the mandibular premolars (both 1st and 2nd premolars) with SEE = 11.07; r2 = 0.540, and P < 0.001 is as follows:
Table 1: Inter Class Correlation and paired t-test to check the reliability of the measured variables (n=30)

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Table 2: Descriptive statistics of the quantitative parameters of the overall samples (n=77)

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Table 3: Pearson's correlation between actual age and parameters in the overall sample (n=77)

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Figure 3: Graphical representation of the actual age vs. the estimated age using Lamendin's original formula

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Figure 4: Scatter plot of the residuals (Estimated age – Actual age) against actual age. (N = 77)

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Table 4: Mean difference between the calendric age and estimated age among the four age groups

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Table 5: Unpaired t-test to test the sex difference in the mean values of the parameters

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Age (Yrs.) =18.423 + 0.441 (P) +0.423 (T)

This equation was tested on a sample of 20 randomly selected mandibular premolars. The mean age of the test sample was 29.98 years (±16.63). The results of the descriptive statistics of the variables in the test sample are presented in [Table 6]. The mean estimated age was 35.85 ± 7.32 years and 37.87 ± 6.19 years by applying the new premolar formula and Lamendin's original formula, respectively [Figure 5]. There was no significant difference in the mean calendric age (A) and the estimated age (B) with the new premolar formula (mean diff = 5.88 years ± 13.15; df19; P > 0.05) [Table 7]. The premolar formula underestimated the actual age in five out of 20 specimens (25%). These specimens belonged to the patients in the age group of 43–70 years with a mean age of 54.75 years. The remaining 75% of the specimens overestimated the actual age and belonged to the patients in the age group of 15–40 years with a mean age of 21.75 years [Figure 6]. The age estimated using Lamendin's original formula (C) was significantly different from the actual age (mean diff = 7.90 yrs.±13.84; df = 19; P < 0.05).
Table 6: Descriptive statistics of the quantitative parameters of the test samples (n=20)

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Figure 5: Graphical representation of the means of the actual age and the estimated ages of the test sample

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Figure 6: Scatter plot of the residuals (B – A) against the Actual age in the test sample (N = 20)

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Table 7: The correlation and the paired t-test in the test sample

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   Discussion Top

We present the application and the validation of Lamendin's dental age estimation method using mandibular premolar teeth on the Western Indian population. The primary finding of this study was that Lamendin's original formula overestimated the age of the overall study samples and showed a strong positive correlation with the root translucency than the periodontosis.[11] The original study by Lamendin in 1992 on a French population reported a mean error of ± 10 years with an r2 value of 0.33. The present study derived a regression equation with a mean standard error of estimate (SEE) 11.07 and an r2 value of 0.540. Though the original Lamendin's method suggested this method for the adult population or individuals above 20 years, we found some degree of dentinal translucency and periodontosis in some of the teeth extracted for orthodontic reasons in adolescents. Hence, in the present study, samples from individuals in the age range of 15–20 years were also included.

Prince and Ubelaker (2002) applied Lamendin's method and modified the regression equation in the non-French population by including the root height parameter in addition to the P and T variables.[16] They concluded that the results were accurate in the age range of 30–69 years with an r2 value of 0.49 (P < 0.001). They also evaluated the effects of sex and population and developed separate regression formulas. In the present study, there was no significant sex difference in the measured variables. Hence, the gender-based regression equations were not generated. However, the present study observed that Lamendin's formula underestimated the age in males, whereas in females, it was overestimated. Lamendin's and Prince and Ubelekar's formulas were tested on a Spanish Caucasian population and in a Columbian population by González-Colmenares et al.[18] (2007). They developed a new population-specific regression model by incorporating the Prince and Ubelaker parameters and obtained an r2 value of 0.488. They applied their population data on all the three formulas, i.e. the original Lamendin's, the modified Prince and Ubelekar's, and their newly derived formulas, and concluded that Prince and Ubelekar's formula generated more accurate age estimation than Lamendin's formula. A recent study by Pulido et al.[22] (2017) compared Lamendin's formula with González-Colmenares' formula in the Mexican population. The mean error was lesser with Lamendin's formula (−17.96) than with González-Colmenares' formula (−37.05). Another study in the Bosnian population reported a mean error of 8.77 years from Lamendin's original formula and 8.42 years from the modified Prince and Ubelaker's formula.[20] Lamendin method in the Peruvian population estimated the age with a mean error of 8.3 years and was reported to be most accurate in the 30–39-year age group.[21] Ackermann and Steyn (2014) tested the accuracy of Lamendin method in the South African population using upper and lower canines. Their report showed that periodontosis was better correlated with age than root transparency. This finding is similar to the results of an Indian study by Acharya and Kumar.[27] The highest correlation between actual age and estimated age in their study was low with r2 = 0.41 and a mean error of 12 and 15 years. However, several studies showed the translucency parameter to be better correlated to actual age than the periodontosis parameter.[16],[18],[20],[21]

In the present study, the root translucency was strongly correlated with the actual age of the overall sample. However, in the test sample, periodontosis correlated better than the translucency factor with the age. Unlike the present study, some Indian studies applied only the translucency parameter in ground-sectioned teeth and revealed a strong positive correlation between dentinal translucency and actual age.[28],[29],[30] Generally, studies testing the accuracy of Lamendin's method have shown an overestimation of age for the younger age groups and an underestimation of age for the older age groups.[18],[19] The modified Lamendin's formula for the mandibular premolars in the present study underestimated the age in 25% of the samples, and all the overestimated samples were below the age of 40 years. Recently, Zorba et al.[31] (2018) reported that Prince and Ubelaker's formula gave more accurate results for specimens with chronological age over 40 years, while Lamendin's formula showed more accurate results for the age groups of 20–29 years and 30–39 years. This was similar to the results of a study by Meinl et al.[32] Their results showed that Lamendin's method overestimated the young age group specimens with a ME of 6.8 years, whereas the middle age group and old age group teeth were underestimated with a ME of − 11.9 and − 26.3, respectively. In the Brazilian population, Lamendin's formula underestimated the age (ME = −8.25) of the overall sample.[15] This is in contrast to the present study in the Western Indian population. Nearly 50% of the samples were overestimated. Nearly 97% of the underestimated cases in the present study belonged to 40 years and above age group. However, they modified the formula for the Brazilian population that yielded a ME of − 0.391 years. They also reported that the mean error is very small in individuals ≤45-years old (ME = 0.61, SD = ±0.97, P > 0.05) than the specimens above 45 years (SE = −19.21, SD ± 2.06, P < 0.001). This is in contrast to the findings of Prince and Ubelaker.[16] Schmitt et al.[33] (2010) used a single tooth from the same reference sample used by Lamendin et al. and Prince and Ubelaker.[21] Their mean error of the age estimation was slightly higher (13.67 years) than the reports of the earlier two studies and to the present study, where the mean error was 11.07 years.

Martrille et al.[34] (2007) compared four skeletal methods for age estimation at death on White and Black adults and found that Lamendin's method was the most accurate method for the age group of 41–60 years. Though there are several studies on the validity of the Lamendin's method in different age groups and on different populations, the acceptance of this method in real forensic cases is still debatable. This may be due to the taphonomical and archeological phenomena. For example, the burial, even for a short period, may influence the accuracy of this method.[35] In addition, the gingival recession, as evident during periodontosis, may not be strictly age-related. Hence, the use of root translucency and the periodontosis parameters as advocated by Lamendin et al. for age estimation should be dealt with cautiously, especially when forensically applying to real-life cases. Moreover, a large sample study on the applicability and reliability of these parameters in the Indian population is highly recommended. A systematic review of Lamendin's dental age estimation method by Lopes et al.[36] revealed that there were certain discrepancies in the methodologies in several studies. This review also recommends a stricter approach in the methodologies when using Lamendin's method of age estimation. Though the present study used the two parameters, periodontosis and dentinal translucency, as suggested by the original Lamendin's article, Foti et al.[37] confirmed the usefulness of the evaluation of dentinal translucency in contrast to the other parameter (periodontosis). The validation of the newly derived regression equation was tested on only a limited number of samples, which the authors consider as one of the limitations of the study. Furthermore, only mandibular premolars were used in the present study. The authors also recommend that a large sample study using multiple teeth from both the arches must be appropriately designed in the future for the validation of Lamendin's adult age estimation method in the Indian population.

Limitations and future recommendations

Tooth-specific and population-specific validation on a large sample size is recommended to strongly authenticate this method for adult dental age estimation using a single tooth.

   Conclusion Top

From the present study, the following conclusions are drawn:

  1. There is a strong positive correlation between the actual age and the estimated age using Lamendin's method.
  2. There is no significant sex difference in the measured parameters in the study sample.
  3. The root translucency parameter is strongly correlated with the actual age than the periodontosis parameter in the study sample.
  4. The new premolar regression equation overestimated the age in 75% of the samples.
  5. Lamendin's method, which applies both the root translucency and periodontosis criteria, may not be a standalone method for adult age estimation. This method can be complemented with other adult age estimation methods as applicable to the query subject in real-life forensic cases.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form the patient (s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.


The authors wish to acknowledge the support of the staff and interns of oral and maxillofacial surgery during sample collection.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

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  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]

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