Abstract
Dental age estimation is a crucial tool in forensic science and legal investigations, used to determine a person’s age based on their teeth. Teeth develop in a predictable manner, making them reliable indicators of age, even when other biological markers are unavailable. This technique is widely applied in criminal cases, identifying victims in mass disasters, and resolving legal disputes related to age, such as immigration cases and age verification for legal purposes. Several scientific methods help in estimating dental age across different age groups. In children and adolescents, there is an evaluation of the developmental stages of permanent teeth through radiographic analysis. Another common approach is the atlas method, which provides a series of reference images showing tooth development at different ages. Demerjian stages in developing teeth to estimate age in children and adolescents. For adults, age estimation relies on regressive changes in teeth, such as root translucency and secondary dentin deposition. These methods provide forensic experts with reliable tools for age estimation, though individual variations in genetics, nutrition, and environmental factors can affect accuracy. Recent advancements in digital imaging, artificial intelligence, and machine learning are improving the precision of dental age estimation. Techniques like Cone Beam Computed Tomography (CBCT) and automated analysis of dental structures are helping forensic scientists refine their assessments. Despite some limitations, dental age estimation remains one of the most effective techniques for age determination when birth records are unavailable or disputed. This review article aims to provide a comprehensive analysis of the various methods used in dental age estimation, highlighting their applications, advantages, and limitations in forensic and legal contexts. It will also explore recent advancements in imaging techniques and artificial intelligence that are enhancing the accuracy of age estimation. By examining existing methodologies and emerging technologies, this review seeks to contribute to the continuous improvement of forensic odontology, ensuring more reliable and standardised age estimation practices for legal and investigative purposes.
Keywords
Introduction
The use of teeth for age estimation dates back to the Factory Act of 1837 in England, which prohibited children with non-erupted permanent second molars from working in factories. In the same year, Edwin Saunders conducted the first scientific study comparing height and dentition in 1,049 children and demonstrated to Parliament that teeth were a more reliable indicator of age than stature. 1 This early work laid the foundation for modern dental age assessment in legal and forensic settings.
Age determination remains essential across multiple domains of forensic and legal practice. In criminal investigations, it helps distinguish juveniles from adults, influencing criminal responsibility, court jurisdiction, and sentencing. It is also critical for identifying unknown human remains when DNA or fingerprints are unavailable. Age assessment plays an equally important role in immigration and asylum proceedings, where determining whether an individual is a minor affects access to services and legal protections. 2 Age additionally governs civil rights and personal autonomy, setting legal thresholds for consent, marriage, contracts, voting, and driving, and is especially significant in sexual offence cases where the ages of both victim and offender determine legal implications.3, 4
Forensic age estimation relies on a range of techniques, including radiographic assessments, dental development analysis, skeletal evaluation, and histological and biochemical methods. 5 These approaches collectively ensure accurate and defensible age assessments, helping maintain legal standards and protect vulnerable populations.
Given the critical importance of dental indicators in age estimation, this review provides an updated synthesis of classical, contemporary, and emerging dental methods (Figure 1). By examining radiological, morphological, histological, and biochemical approaches—along with recent advancements such as population-specific formulas and improved atlas techniques—this article supports more accurate, standardised, and scientifically robust age estimation in forensic practice.
Classification of Age Estimation 5 .
Material and Methodology
The databases searched were Google Scholar, PubMed and the terms used were ‘dental age estimation methods’ and ‘forensic dentistry’. The studies listed from these terminologies were analysed. Publicly available, peer-reviewed research on dental age estimation techniques which met the inclusion requirements and were found in accordance with the objective of the authors was included in the study.
The various classifications for dental age estimation based on various parameters are as follows.
Method of application, whether on living or deceased (Figure 2). 6
Various modalities, that is, visual, radiographic, biochemical (Figure 3). 7
Classification of Methods Based on Living and Deceased 6 .
Classification Based on Various Modalities for Age Estimation 7 .
Classification Based on Category of Individual on Age 8 .
Dental Age Estimation Techniques
The following categories have been employed to group several distinct methodologies:
Radiological methods. a. Atlas Method b. Scoring method Visual methods. Histological methods. Biochemical methods.
Radiological Methods
Altas Method
In this, the individual’s dental X-ray is matched with pre-established reference images that represent different stages of dental development. The atlas provides a standardised chronological sequence of tooth formation, helping estimate age based on observed dental maturity. The majorly used atlas is Schour and Masseler and Ubelkar. In recent times, the London atlas is considered to be a more appropriate method of age estimation in developing dentition. 9
Schour and Masseler Method
Schour and Masseler tracked the development of permanent and deciduous teeth in 1941, creating development charts that depicted 21 chronological stages from 4 months to 21 years of age. There are no separate charts for men and women because these charts lack individual surveys for each gender (Figure 5). 10 The major advantage is the visual representation of various stages of teeth development. The estimated age is in a narrow range of +6 months.
Schour and Massler Dental Development Chart 10 .
Ubelkar Method
This method was developed by Douglas H. Ubelaker in 1978 to better suit Native American dental development. The original chart, based on European samples, often overestimated age (Figure 6). In 1989, Ubelaker refined the age ranges to account for natural variations in dental development among individuals of the identical age. Ever since then, this method has become a widely accepted standard for data collection. However, because it was developed using archaeological samples and tailored for Native American populations and broader age groups, it may not be fully accurate for modern or other ancestry groups. 11 The noteworthy inclusion of 5 months intra-uterine life to 35 years of age broadens the scope of its application in a wide range of individuals. Also, the narrow standard deviation of age range at early stages of life and broader in later stages highlights that as age advances, the efficiency to provide a small age range declines.
Ubelkar Dental Development Chart 11 .
London Atlas Method
This was held in the Natural History Museum in London, UK, and the Royal College of Surgeons in England. For individuals between 28 weeks in utero and 23 years old, a thorough evidence-based atlas was created to determine age using both tooth maturation and alveolar eruption (Figure 7). There are no gaps or overlaps in the series of pictures that depict a continuum of developmental ages. To solve earlier shortcomings of dental atlases and provide more specificity in age predictions, the London atlas was created on a sample of British White and Bangladeshi people.
11
It has taken into consideration not on the development stage of primary and permanent teeth but also the resorptive stages of primary teeth and the emergence level at the alveolar bone. They have also developed an online software application at
The London Atlas Dental Development Chart 11 .
Scoring Methods
Demirjian’s Method
A popular approach for determining a child or adolescent’s age based on the development of their teeth is the Demirjian dental age estimation method. The original method is based on a seven-stage approach of dental growth using radiographic pictures of the mandibular teeth and was first presented by Arto Demirjian et al. in 1973. It is considered as gold standard for age estimation in the adolescent age group. 12
Indian Formula
In a study conducted by Ashith Acharya, 13 the age predicted by Demerjian’s eight teeth regression equation predicted inferior age than the documented age; hence, it was emphasised to develop population-specific formulas. The Indian population specified formula is.
Male: Age = 27.4351 − (0.0097 × S2) + (0.000089 × S3).
Female: Age = 23.7288 − (0.0088 × S2) + (0.000085 × S3).
The earlier Demirjian method had eight stages considering seven teeth, and the later had 10 stages of teeth development with the inclusion of 3rd molar. The Indian population-based developed formula surpassed the limitation of overestimation of age and exhibited improved age estimation concerning our population with biologically sex specific formulas.
Gleizer and Hunt Method
In permanent dentition, most teeth have undergone development by 12 years; hence, to determine the most crucial age group, the development of third molars in 17 stages, as stated by Gleizer and Hunt, is of immense importance to be taken into consideration. In a study conducted by Asmidha et al., a high association between age and stages was obtained. 14 The major advantage is that, unlike other method which considers the staging of multiple teeth, in case of congenital absence or extraction of teeth, it questions the applicability of the technique considering the contralateral tooth.
Leiff Kulman Method
Leif Kullman considered the root development of the third molar. In the Indian population, it was found that in the root development between stages 1 and 5, the subject had not attained the age of 18. If the stage of root development was found to be 7, one can be assured that the subject has attained 18 years of age. 15 The maturity stage of the third molar can be appreciated in not just dental radiographs but also lateral cephalograms, which are readily available in a medical set-up. In this technique, only staging the third molar presents it as a convenient method of dental age estimation, which is extremely significant, especially in juvenile cases.
Visual Methods
Teeth Eruption
Human dentition is diphyodont, heterodont and gomphosis. There are primary teeth and permanent teeth. The sequence of eription of teeth has been depicted in Table 1. Between the ages of 6 and 12 is known as the transition or mixed dentition stage. 16
Primary Teeth Eruption and Exfoliation 16 .
The major advantage is that a mere visual oral examination without any aids, such as a radiographic unit and processing of the sample, is needed for assessment of the eruption of teeth. A trained expert who can differentiate primary and permanent teeth and knows the sequence of teeth can examine and estimate the age.
Colour of Teeth
The colour of enamel is influenced by age; as age advances, the value is decreased and chroma increases. A transition to a more reddish colour is observed with an increase in age. The translucency, thickness, and chemical composition of enamel and dentin combine to form enamel shade. Because the characteristics and thickness of dentin and enamel are always changing, the optical character changes with age. As we age, attrition causes enamel to weaken, and secondary dentin deposition causes dentin to develop. 17 The technique cannot the applied to individuals with habits such as tobacco chewing.
The examination of virgin teeth alongside a reference shade guide, with simple training provided to the observer, can help in age estimation. The white and yellow hues are more associated with younger age, and reddish brown and grey hues are associated with older age.
Li and Ji Method
A novel approach to age estimation based on permanent molars, known as the Average Stage of Attrition (ASA) method, was introduced (Figure 8). A new grading system for molar crown attrition was developed, and six linear regression equations for age estimation were established (Table 2). With a maximum inaccuracy of 4.53 years, the ASA method enables age assessment using a single molar, either M₁ or M₂, from the maxilla or mandible. By averaging wear stages across all cusps rather than concentrating on a single cusp or a selection of cusps, ASA offers a more objective assessment of occlusal surface attrition than other dental wear methods. 18
Regression Equation for Age Estimation Based on the Li and Ji Method 18 .
Stages of Crown Attrition from 0 to 9. 18
The molar teeth are not subjected to post-mortem loss by virtue of being multi-rooted and the anchorage they have in the alveolar bone. Therefore, on occlusal and buccal inspection of teeth, and staging the level of attrition without any other equipment is a boon of the technique.
Histological Methods
The assessment of teeth is the foundation of histological techniques. For microscopic preparation, these techniques necessitate the extraction of teeth. However, for ethical, religious, or cultural reasons, these approaches might not be appropriate.
Gustafson’s Method (1950) depicted the age changes occurring in the dental tissues and recorded six changes related to age. 19
They are:
Attrition on incisal or occlusal surfaces (A) Periodontitis (P) Secondary dentin formation (S) Apposition of Cementum (C) Resorption of roots (R) Transparency or root (T)
Gustafson recommended the last two changes. In the method proposed, each sign was ranked and allotted 0, 1, 2, or 3 points.
The point values of each age-change are added according to the following formula:
Points are An + Pn + Sn + Cn + Rn + Tn. According to the preceding formula, the precise equation that was computed was y = 11.43 + 4.56x, where y = age and x = points. Gustafson (1950) calculated an error of speculation of ±3.6 years. Its inability to be effectively applied to living individuals is a disadvantage. This is the pioneer method of dental age based on the above-mentioned parameters; most of the histological methods are a modification of Gustafson’s method.
Dalitz Method
Dalitz revisited Gustafson’s method and introduced a revised 5-point system, ranging from 0 to 4, as opposed to the previous 4-point system. This modification aimed to enhance accuracy. The findings indicated that root resorption and secondary cementum formation were not significant factors. The remaining criteria—attrition (A), periodontitis (P), secondary dentine (S) deposition, and root transparency (T)—were found to be strongly correlated with age, exhibiting similar levels of association. Dalitz proposed the following formula for age estimation:
E = 8.691 + 5.146A + 5.338P + 1.866S + 8.411T
The study did not include bicuspids and molar teeth. 20
Bang and Ramm Method
They discovered that, beginning at the root’s tip and moving coronally with age, the root dentine appears to become transparent during the third decade. It was discovered that throughout the third decade, the transparency of the root dentin advances coronally from the root tip. A great advantage of the method is that good results are obtained by measuring intact roots. 20
Johanson Method
Age changes were differentiated into seven different stages (A0–A3) and evaluated for the same six criteria mentioned earlier: attrition (A), secondary dentine formation (S), periodontal attachment loss (P), cement apposition (C), root resorption (R), and apical translucency (T). Johanson made a more detailed study of the root transparency and stated that it is clearer when the thickness of the ground section of the tooth is 0.25 mm.
The following formula was recommended: Age = 11.02 + (5.14 × A) + (2.3 × S) + (4.14 × P) + (3.71 × C) + (5.57 × R) + (8.98 × T) 20
Maples Method
In this method, only two of the total six Gustafsons were taken into consideration. Translucency of root and secondary dentine formation was selected out of the six criteria of evaluation, which made the method simpler and accurate.
Solheim Method
Except for root resorption, Solheim considered the other five parameters, that is, attrition, secondary dentin formation, periodontitis, cementum apposition, and root transparency, with the inclusion of three new age-related changes—surface roughness, colour, and sex that showed a significant correlation in different types of teeth. 20
Biochemical Methods
Carbon 14
C dating in dental enamel is made possible by the significant and rapid increase in the level of 14C in the atmosphere due to atmospheric nuclear tests carried out during the Cold War from 1955 to 1963. Before that, the levels of C in the atmosphere had remained constant over the last millennia. Atmospheric nuclear tests resulted in an increase in C in the troposphere, which then entered the atmosphere and was dispersed over the entire globe. Nuclear explosions introduced into the atmosphere approximately 2.1 × 1017 Bq of 14C, that is, a doubling of the 14C/C ratio in the atmosphere. After the Limited Test Ban Treaty was signed in 1963, atmospheric nuclear tests were banned. From then on, the level of atmospheric 14C began to decrease exponentially, not because of radioactive decay but because of the progressive incorporation of 14C into marine and terrestrial reservoirs. Thus, atmospheric C reacts with oxygen to form CO2, which is incorporated into plants by photosynthesis. By eating these plants and animals feeding on these plants, the concentration of atmospheric carbon in the human body, especially in tooth enamel, evolves at all times in a quasi-parallel manner with the increase in 14C levels in the atmosphere. Therefore, based on this principle, age is estimated. 18 Various studies have cited that age can be claimed with an accuracy of age by the mean error of ±1.5 years. 21
Aspartic Acid Racemisation
The use of aspartic acid racemisation for age assessment was first introduced by Helfman and Bada in 1975 and has since become a widely utilised method in forensic age estimation. As individuals age, L-amino acids gradually convert to their D-forms through racemisation. At a temperature of +25°C, complete racemisation of all L-amino acids in living tissues would take approximately 100,000 years. This process allows the degree of racemisation to serve as a reliable indicator for estimating the age of various tissues. Among stable amino acids, aspartic acid exhibits one of the fastest racemisation rates, making it the preferred choice for age estimation. The rate of L- to D-form conversion is influenced by factors such as temperature, humidity, and pH. Since continuous amino acid turnover affects accuracy, tissues with low metabolic rates provide more precise age estimates. Given these considerations, teeth are the preferred tissue for forensic age estimation. In cases where the post-mortem interval is extended, forensic experts often rely on bones and teeth as the primary sources for analysis.
It is an advanced method of age estimation with involves technique-sensitive instrumentation such as gas chromatography and high-performance liquid chromatography. The extent of preservation on aspartic acid racemisation in deceased, in case where the dentin of healthy, impacted, and carious teeth was studied and noted that the teeth can be preserved for up to 10 years, showing a negligible effect on estimated values with an error of 4 years. 22
Conclusion
Dental age estimation techniques play a crucial role in forensic science, anthropology, and clinical dentistry by providing reliable age assessments based on dental development and changes. Various methods, including radiographic, morphological, and biochemical approaches, have been extensively studied and applied to different populations. Traditional techniques such as Demirjian’s remain widely used due to their simplicity and accuracy, while newer advancements incorporating artificial intelligence and machine learning show promise for enhanced precision. Despite their effectiveness, these methods have limitations, including population-specific variations, observer subjectivity, and differences in environmental and genetic influences. Future research should focus on refining existing techniques, integrating digital and AI-based approaches, and developing more standardised protocols for broader applicability. Continued advancements in dental age estimation will improve accuracy, making it an indispensable tool in forensic and medical fields.
Footnotes
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Ethical Approval
As it is a review article, ethical approval is not applicable.
Funding
The authors received no financial support for the research, authorship and/or publication of this article.
Informed Consent
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