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. 2023 Apr 5;20(1):59–72. doi: 10.1007/s12024-023-00587-5

Dental age estimation: development and testing of a reference data set for Qatari children, adolescents, and young adults aged between 5 and 25 years

Noof Al-Obaidli 1,2, Najat Al-Hashimi 2, Victoria S Lucas 1, Graham Roberts 1,
PMCID: PMC10944394  PMID: 37020085

Abstract

The purpose of this study is to establish and test a reference data set of dental development of Qatari subjects aged between 5 and 25 years. Radiographs of individuals aged between 5 and 25 years were re-used to establish a reference data set (RDS). A scheme comprising 8 tooth development stages (TDS) was used to assess all the teeth on the left side of the maxilla and mandible. The accuracy of dental age estimation (DAE) was tested with a separate sample of radiographs – the validation sample (VS) comprised 50 females and 50 males of known chronological age (CA). Dental panoramic tomographs (DPT) of 1,597 Qataris were assessed. The summary data for the individual TDS comprising the number (n-tds), mean (x¯-tds), standard deviation (sd-tds), 0th%-ile (the minimum), 25th%-ile, 50th%-ile (the median), 75th%-ile, and 100th%-ile (the maximum) were used to estimate the age of the VS subjects using the simple average method (SAM). There is a significant difference in dental age of 4.8 months in the female group when compared to the CA. The difference in the male group is 4.5 months. This shows similar differences to assessments of other ancestral or ethnic groups.

Keywords: Reference data set, Tooth development stages, Dental age estimation, Qatari population, Demirjian tooth development stages

Introduction

There has been a significant increase in illegal migration during the last 20 years. Many migrants do not have identification documents or a record of their date of birth. Age estimation (AE) is important for legal, social, forensic, and archaeological purposes.

Dental age estimation offers a biological state that mimics chronological age thus enabling the following:

  1. Estimation of the chronological age (CA) of an individual with unknown date of birth or missing identification documents. In addition to the point estimate of age, it is important to indicate the uncertainty associated with the estimate. Age estimation of these individuals is necessary to establish eligibility for civil rights, education, or social benefits.

  2. A further need for AE is in criminal proceedings where the judicial process is determined by the age of the accused.

  3. To provide reference standards for different ethnic, ancestral, racial, and identifiable human groups.

The United Nations Convention on the Rights of the Child (UNCRC 1989) [1] is a widely recognised international treaty that has changed and improved the way children are perceived across the globe. Young people aged less than 18 years are cared for under regulations pertaining to minors. There are large numbers of refugees of uncertain age who do not have birth records. Age estimation is necessary to ensure that social care, healthcare, and education are appropriately provided. There is also a safeguarding issue because adult asylum seekers who are over 18 years but claiming to be under 18 years of age should not be domiciled in the same accommodation as minors. DAE may assist in cases of child trafficking. Underage girls have been forced into marriage, and in these cases, traffickers claim that the children are older than they appear.

Age estimation may be important in criminal proceedings as some criminals, to avoid being punished as adults, claim to be younger than they look. This is so these young adults serve a sentence under the juvenile penal code. In Qatar, the age of criminal responsibility for children is between 7 and 17 years of age. This is stipulated by the Supreme Judiciary Council in Qatar.

There have been many attempts to devise schemes to estimate the age of children [2, 3] In a paper published in 1973 [2] it was stated that “ … it should be remembered that the sample on which they are based is of entirely French-Canadian origin. The dental maturity scores for a given chronological age may well be greater or less in other populations …” This is an important and often overlooked statement.

The purpose of this study is to use extant dental panoramic tomographs (DPT) to prepare a reference data set (RDS) for Qatari children, adolescents, and young adults to create an ethnic-specific dataset to enable assessment of dental maturity in Qatari subjects and from this to estimate dental age (DA) with the associated range of uncertainty.

Materials and methods

Reference data set

The RDS comprised 1600 DPTs of female and male Qatari children, adolescents, and young adults. The DPTs were retrieved from the radiological archives of Rumailah Hospital and Al-Wakra Hospital outpatient departments as well as an orthodontic office. The chronological age of each subject was calculated using the date of birth and the date of radiographic exposure of the DPT. Digital copies of the DPTs were transferred from Qatar using a portable password protected storage device. This was to ensure and protect anonymity. Ethical approval was granted by the Hamad Medical Corporation Research Centre in Qatar [Research protocol #1518/15 “Dental Age Assessment” Ref No. MRC 1535/2016.].

Exclusion criteria

  • Non-Qatari subjects

  • Poor quality DPTs

  • Subjects with a medical condition that may affect dental maturation

Tooth development stages

These were assessed using the 8 stage system described by Demirjian, Goldstein, and Tanner in 1973 (Fig. 1 and Table 1) [2]. The date of birth and sex was blinded from the assessor at the time of the assessment.

Fig. 1.

Fig. 1

The 8 stages of dental development described by the Anglo-Canadian research team. These descriptions should be used when viewing dental panoramic tomographs to determine the stage of development from A to H

Table 1.

Written guide of morphologically related descriptions of Demirjian’s TDS

Tooth development stage (TDS) Single rooted teeth and multi-rooted teeth [descriptions]
A In both uniradicular and multiradicular teeth, a beginning of calcification is seen at the superior level of the crypt in the form of an inverted cone or cones. There is no fusion of these calcified points
B Fusion of the calcified points forms one or several cusps, which unite to give a regularly outlined occlusal surface
C

a. Enamel formation is complete at the occlusal surface. Its extension and convergence toward the cervical region is seen

b. The beginning of a dentine deposit is seen

c. The outline of the pulp shape has a curved shape at the occlusal border

D

a. Crown formation is complete down to the cemento-enamel junction

b. The superior border of the pulp chamber in uniradicular teeth has a definite curved form, being concave towards the cervical region. The projection of the pulp horns, if present, gives an outline like an umbrella top. In molars, the pulp chamber has a trapezoid form

c. Beginning of root formation is seen in the form of a radiopaque spicule

E

Uniradicular teeth

a. The walls of the pulp chamber now form straight lines, whose continuity is broken by the presence of the pulp horn, which is larger than in the previous stage

b. The root development is less than the crown

Multiradicular teeth

a. Initial formation of the radicular bifurcation is seen in the form of either a calcified point or a semilunar shape

b. The root length is less than the crown height

F

Uniradicular teeth

a. The walls of the pulp chamber now form a more or less isosceles triangle. The apex ends in a funnel shape

b. Root development is equal to or greater than the crown

Multiradicular Teeth

a. The calcified region of the bifurcation has developed further down from its semilunar stage to give the roots a more definite and distinct outline, with funnel shaped endings

b. The root length is equal to or greater than the crown height

G

a. The walls of the root canals are now parallel (distal root of molars)

b. The apical ends of the root canals are still partially open

H

a. The apical end of the root canal is completely closed (distal root in molars)

b. The periodontal membrane has a uniform width around the root and apex

The TDS were recorded on a specially designed form and then entered into a Microsoft Access database. The assessment included all the teeth on the left side of both the maxilla and mandible. Teeth that were fully developed (stage H) were recorded but excluded from calculations for DAE (Fig. 2).

Fig. 2.

Fig. 2

DPT for subject ID 47 female showing the TDS assessed for this subject. Teeth on the left side only are used. The tooth development stages are indicated in the upper case type on the image

Observer agreement

The primary investigator (NAO) recorded the TDSs on the left side of 10 DPTs, comprising 160 assessments. These DPTs were separate from the main study. The assessments were on 2 occasions 2 weeks apart to determine within observer agreement (WOA). A second observer (VSL) assessed the same DPTs to determine between observer agreement (BOA). Both the WOA and the BOA were calculated using Cohen’s kappa [4], and the outcome index was assessed according to the categories suggested by Landis and Koch in 1977 [5].

Data processing

The major part of the study was to establish a RDS for the young Qatari population. The TDSs (A through H) of each tooth present on a sample radiograph were entered into a Microsoft Access database. To extract data from the database, queries were created which included the following information: ID number, ethnicity, sex, and age at assessment (AaA) of each TDS.

These queries were exported to Excel and a small worksheet was created for each TDS for both females and males, for example, UL8Gf [Upper Left Third molar, Stage G, female]. Each of these queries comprised the dataset for that specific stage from which the number (n-tds), the mean (x¯-tds), standard deviation (sd-tds), 0th%-ile (minimum), 25th%-ile, 50th%-ile (median), 75th%-ile, and the 100th%-ile (maximum) were calculated.

Validation study

The second part of this work was to assess the accuracy of the method of DAE when utilizing the Qatari RDS. The validation sample (VS) comprised 100 DPTs of Qatari children and adolescents. There were 50 females and 50 males with known date of birth and date of radiographic exposure. These data were blinded from the assessor. The radiographs were separate from the RDS. The TDS from each tooth on the left side was entered into an Excel worksheet which semi-automatically looked up the n-tds, x¯-tds,andsd-tds, from the RDS to calculate the DA to enable comparison of the CA with the DA for each VS subject [6]. When the 50 female and 50 male subjects had been assessed, CA and DA were compared using Student’s t test.

The distribution of individual TDS for subject 47f, gives the estimated DA with the variation of the individual TDS (Fig. 3). In addition to this formal statistical test, Bland–Altman graphs were used to show the variation of the difference between CA and DA around the line of no difference (Figs. 4 and 5) [7].

Fig. 3.

Fig. 3

Forest plot showing the variation around the median value for DA using the 25th to 50th percentiles

Fig. 4.

Fig. 4

Bland–Altman plot demonstrating that the spread of DA values is evenly distributed around the mean value of the CA (for females)

Fig. 5.

Fig. 5

Bland–Altman plot demonstrating that the spread of DA values is evenly distributed around the mean value of the CA (for males)

Results

Sample distribution

The number of female and male subjects by 1 year age bands is shown in Table 2.

Table 2.

Numbers of subjects contributing radiographs by 1 year age spans A total of 1,597 subjects spread fairly evenly between females and males comprised the DPT from subjects resident in Qatar

Age span Number of females Number of males
6.00–6.99 2 4
7.00–7.99 21 19
8.00–8.99 48 47
9.00–9.99 42 59
10.00–10.99 123 86
11.00–11.99 122 110
12.00–12.99 80 92
13.00–13.99 40 99
14.00–14.99 31 47
15.00–15.99 17 58
16.00–16.99 20 32
17.00–17.99 14 54
18.00–18.99 21 15
19.00–19.99 19 11
20.00–20.99 22 8
21.00–21.99 27 10
22.00–22.99 39 11
23.00–23.99 31 18
24.00–24.99 26 16
25.00–25.99 37 19
Total 782 815

Observer agreement

The WOA score for TDS was 0.9203 and the BOA score for TDS was 0.8401 demonstrating an almost perfect agreement [5].

Reference data set

Radiographs of 1597 Qatari children, adolescents, and young adults age between 5 and 25 years were assessed by the main investigator (NAO) to create the RDS. The data for females and males across the whole range of the age are shown in Table 3 comprising normal distribution summary statistics and percentile distribution summary statistics.

Table 3.

Qatari RDS for 782 females and 815 males

All numerical data in years
TDS n-tds x¯-tds sd-tds 0%ile (min) 25th%ile 50th%ile 75%ile 100%ile (max)
UR8Af 5 9.67 0.54 8.99 9.42 9.52 10.16 10.28
UR8Am 8 9.50 0.78 8.02 9.13 9.91 9.98 9.98
Difference f minus m = 0.17, 95% CL =  −0.711 to 1.051, t = 0.425, p > 0.679 ns
UR8Bf 50 9.90 1.23 7.47 8.97 9.89 10.63 12.51
UR8Bm 37 9.98 0.97 8.12 9.18 9.98 10.54 11.90
Difference f minus m =  −0.08, 95% CI =  −0.566 to 0.406, t =  −0.327, p > 0.744 ns
UR8Cf 85 10.89 1.16 8.19 10.16 10.83 11.51 13.54
UR8Cm 106 11.15 1.16 8.52 10.38 11.18 11.89 13.65
Difference f minus m =  −0.26, 95% CI =  −0.593 to 0.073, t =  −1.539, p > 0.125 ns
UR8Df 156 12.13 1.45 9.17 11.09 12.12 12.89 18.25
UR8Dm 173 12.65 1.48 8.58 11.60 12.79 13.43 18.12
Difference f minus m =  −0.52, 95% CI =  −0.838 to −0.201, t =  −3.212, p > 0.001 ns
UR8Ef 38 13.73 1.14 11.52 12.86 13.85 14.49 16.19
UR8Em 93 14.03 1.43 11.26 13.30 13.81 14.69 18.64
Difference f minus m =  −0.3, 95% CI =  −0.815 to 0.215, t =  −1.15, p > 0.251 ns
UR8Ff 24 16.23 1.62 13.71 14.82 16.21 17.34 19.19
R8Fm 39 15.80 1.32 13.32 15.20 15.75 16.98 19.19
Difference f minus m = 0.43, 95% CI =  −0.32 to 1.18, t = 1.15, p > 0.25 ns
UR8Gf 33 17.75 2.05 14.81 16.28 17.10 18.92 21.98
UR8Gm 65 16.61 1.22 11.76 15.96 16.75 17.37 18.93
Difference f minus m = 1.14, 95% CI = 0.48 to 1.79, t = 3.45, p > 0.00 ns
UL1Ef 1 7.51 7.52 7.52 7.52 7.52 7.52
UL1Em 4 7.94 0.80 7.14 7.25 7.98 8.63 8.65
Unable to compute
UL1Ff 26 7.86 0.67 6.86 7.36 7.86 8.24 9.42
UL1Fm 33 8.04 0.72 6.32 7.67 8.05 8.53 9.67
Difference f minus m =  −0.18, 95% CL =  −0.547 to 0.187, t =  −0.983, p > 0.33 ns
UL1Gf 26 8.63 0.79 7.47 8.20 8.53 8.88 10.38
UL1Gm 39 8.95 0.97 7.21 8.28 8.88 9.80 10.83
Difference f minus m =  −0.32, 95% CI =  −0.77 to 0.14, t =  −1.39, p > 0.166 ns
UL2Df
UL2Dm 2 7.70 0.48 7.35 7.35 7.70 8.04 8.04
Unable to compute
UL2Ef 9 7.30 0.30 6.86 7.13 7.25 7.52 7.78
UL2Em 18 7.95 0.68 6.32 7.66 8.13 8.53 8.65
Difference f minus m =  −0.65, 95% CI =  −1.14 to −0.16, t =  −2.72, p > 0.012 ns
UL2Ff 32 8.25 0.56 7.22 7.93 8.25 8.67 9.42
UL2Fm 47 8.56 0.92 6.87 8.02 8.52 9.18 10.83
Difference f minus m =  −0.31, 95% CI =  −0.67 to 0.05, t =  −1.70, p > 0.092 ns
TDS n- tds x¯-tds sd-tds 0%ile (min) 25th%ile 50th%ile 75%ile 100%ile (max)
UL2Gf 47 9.53 1.01 7.57 8.59 9.42 10.31 11.92
UL2Gm 75 10.01 0.89 7.86 9.32 10.12 10.66 11.92
Difference f minus m =  −0.48, 95% CI =  −0.83 to −0.13, t =  −2.75, p > 0.006 ns
UL3Df 2 7.32 0.27 7.13 7.13 7.32 7.52 7.52
UL3Dm 2 7.29 0.09 7.23 7.23 7.29 7.35 7.35
Difference f minus m = 0.03, 95% CL =  −0.84 to 0.89, t = 0.15, p > 0.895 ns
UL3Ef 19 7.76 0.59 6.86 7.25 7.64 8.20 8.95
UL3Em 41 8.09 0.71 6.32 7.66 8.11 8.62 9.35
Difference f minus m =  −0.33, 95% CI =  −0.71 to 0.05, t =  −1.76, p > 0.083 ns
UL3Ff 94 9.39 1.09 7.36 8.56 9.21 10.26 12.13
UL3Fm 156 9.94 0.96 7.74 9.19 10.05 10.54 11.90
Difference f minus m =  −0.55, 95% CI =  −0.81 to −0.29, t =  −4.17, p > 0 ns
UL3Gf 208 10.82 0.81 0.81 8.26 10.22 10.86 13.23
UL3Gm 141 11.51 0.92 8.98 11.01 11.49 12.06 13.99
Difference f minus m =  −0.69, 95% CI =  −0.87 to −0.51, t =  −7.34, p > 0 ns
UL4Df 13 7.61 0.62 6.86 7.22 7.40 7.78 8.88
UL4Dm 23 7.86 0.73 6.32 7.22 7.94 8.47 9.32
Difference f minus m =  −0.25, 95% CI =  −0.74 to 0.24, t =  −1.04, p > 0.31 ns
UL4Ef 37 8.54 0.83 6.99 8.11 8.39 8.81 10.38
UL4Em 56 8.69 0.79 6.97 8.22 8.61 9.21 10.32
Difference f minus m =  −0.15, 95% CI =  −0.49 to 0.19, t =  −0.88, p > 0.38 ns
UL4Ff 76 9.57 0.85 7.89 8.93 9.61 10.25 11.29
UL4Fm 78 9.89 0.73 7.86 9.23 9.98 10.29 11.69
Difference f minus m =  −0.32, 95% CI =  −0.57 to −0.067, t =  −2.51, p > 0.013 ns
UL4Gf 109 10.77 0.81 8.86 10.22 10.64 11.29 13.36
UL4Gm 93 10.98 0.80 8.84 10.45 10.96 11.50 13.35
Difference f minus m =  −0.21, 95% CI =  −0.43 to 0.01, t =  −1.85, p > 0.067 ns
UL5Df 13 7.68 0.72 6.86 7.15 7.37 8.24 8.88
UL5Dm 21 7.84 0.76 6.32 7.23 7.86 8.47 9.33
Difference f minus m =  −0.16, 95% CI =  −0.69 to 0.38, t =  −0.61, p > 0.55 ns
UL5Ef 54 8.83 0.95 7.36 8.13 8.58 9.59 10.49
UL5Em 78 9.06 0.99 6.97 8.28 8.98 9.97 11.67
Difference f minus m =  −0.23, 95% CI =  −0.57 to 0.11, t =  −1.33, p > 0.18 ns
UL5Ff 118 10.26 0.98 7.89 9.64 10.31 11.08 12.68
UL5Fm 120 10.39 0.89 8.16 9.82 10.40 11.01 12.09
Difference f minus m =  −0.13, 95% CL =  −0.37 to0.11, t =  −1.07, p > 0.29 ns
UL5Gf 151 11.15 0.94 8.86 10.38 11.09 11.92 13.36
UL5Gm 100 11.59 0.95 8.84 11.04 11.61 12.31 13.81
Difference f minus m =  −0.44, 95% CI =  −0.68 to −0.20, t =  −3.62, p > 0.00 ns
TDS n- tds x¯-tds sd-tds 0%ile (min) 25th%ile 50th%ile 75%ile 100%ile (max)
UL6Ef
UL6Em 1 6.32 6.32 6.32 6.32 6.32 6.32
Unable to compute
UL6Ff 2 7.39 0.36 7.13 7.13 7.39 7.64 7.64
UL6Fm 1 8.05 0.76 8.05 8.05 8.05 8.05 8.05
Unable to compute
UL6Gf 25 7.93 0.74 6.99 7.40 7.78 8.30 10.38
UL6Gm 33 8.11 0.85 7.03 7.41 8.11 8.56 11.67
Difference f minus m =  −0.18, 95% CI =  −0.61 to 0.25, t = 0.84, p > 0.40 ns
UL7Df 24 7.91 0.76 6.86 7.31 7.71 8.27 9.59
UL7Dm 42 8.22 0.97 6.32 7.66 8.08 8.72 11.67
Difference f minus m =  −0.31, 95% CI =  −0.77 to 0.15, t =  −1.35, p > 0.18 ns
UL7Ef 108 9.56 1.16 7.36 8.64 9.56 10.25 14.51
UL7Em 129 9.81 1.08 7.03 9.06 9.95 10.38 13.35
Difference f minus m =  −0.25, 95% CI =  −0.54 to 0.04, t =  −1.72, p > 0.09 ns
UL7Ff 100 10.74 0.96 8.77 10.25 10.61 11.27 13.36
UL7Fm 73 10.91 0.99 8.58 10.34 10.96 11.62 12.77
Difference f minus m =  −0.17, 95% CI =  −0.47 to 0.13, t =  −1.14, p > 0.26 ns
UL7Gf 167 11.49 0.98 9.61 10.80 11.49 12.13 16.95
UL7Gm 173 12.20 1.12 9.06 11.39 12.11 13.03 15.57
Difference f minus m =  −0.71, 95% CI =  −0.93 to −0.49, t =  −6.21, p > 0.0 ns
UL8Af 5 9.35 0.57 8.65 8.99 9.42 9.52 10.16
UL8Am 9 9.78 1.02 8.02 9.40 9.91 10.19 11.17
Difference f minus m =  −0.43, 95% CL =  −1.52 to0.66, t =  −0.86, p > 0.41 ns
UL8Bf 47 9.83 1.14 7.47 8.97 9.92 10.62 12.35
UL8Bm 36 10.13 1.19 8.12 9.20 9.98 10.88 13.19
Difference f minus m =  −0.3, 95% CI =  −0.81 to 0.21, t =  −1.17, p > 0.25 ns
UL8Cf 95 10.93 1.16 8.19 10.16 10.89 11.54 13.65
UL8Cm 106 11.06 1.10 8.67 10.29 11.03 11.88 14.04
Difference f minus m =  −0.13, 95% CI =  −0.44 to 0.18, t =  −0.82, p > 0.42 ns
UL8Df 149 12.13 1.37 9.17 11.13 12.14 12.84 17.70
UL8Dm 178 12.69 1.62 8.12 11.60 12.79 13.49 18.97
Difference f minus m =  −0.56, 95% CI =  −0.89 to −0.23, t =  −3.34, p > 0.00 ns
UL8Ef 41 13.89 1.39 11.52 12.93 13.86 14.79 19.19
UL8Em 94 14.00 1.39 11.26 13.30 13.80 14.68 18.64
Difference f minus m =  −0.11, 95% CI =  −0.62 to 0.40, t =  −0.42, p > 0.67 ns
UL8Ff 19 16.03 1.57 13.65 14.59 16.00 17.24 19.69
UL8Fm 36 15.76 1.13 13.32 15.30 15.75 16.81 17.45
Difference f minus m = 0.27, 95% CI =  −0.47 to 1.00, t = 0.73, p > 0.47 ns
TDS n- tds x¯-tds sd-tds 0%ile (min) 25th%ile 50th%ile 75%ile 100%ile (max)
UL8Gf 32 17.51 2.00 14.81 16.19 16.88 18.57 21.97
UL8Gm 64 16.68 1.06 14.44 15.96 16.70 17.36 18.93
Difference f minus m = 0.83, 95% CI = 0.21 to 1.45, t = 2.67, p > 0.01 ns
LR8Af 35 9.89 1.25 7.47 8.94 10.08 11.29 11.92
LR8Am 26 9.69 1.15 7.77 8.68 10.07 10.46 11.44
Difference f minus m = 0.2, 95% CI =  −0.43 to 0.83, t = 0.64, p > 0.53 ns
LR8Bf 46 9.97 1.22 7.93 8.95 10.11 10.50 13.08
LR8Bm 32 10.01 1.07 8.12 9.28 9.98 10.76 12.69
Difference f minus m =  −0.04, 95% CI =  −0.57 to 0.49, t =  −0.15, p > 0.88 ns
LR8Cf 95 10.90 1.14 8.19 10.16 10.62 11.64 13.65
LR8Cm 107 10.92 1.30 8.52 10.05 10.67 11.75
Difference f minus m =  −0.02, 95% CI =  −0.36 to 0.32, t =  −0.12, p > 0.91 ns
LR8Df 176 12.11 1.39 9.17 11.16 11.95 12.88 18.25
LR8Dm 208 12.68 1.43 8.58 11.61 12.77 13.51 18.53
Difference f minus m =  −0.57, 95% CI =  −0.85 to – 0.29, t = -3.94, p > 0.00 ns
LR8Ef 37 14.21 1.33 11.97 13.30 14.13 14.85 18.39
LR8Em 78 14.27 1.37 11.40 13.45 13.84 15.20 18.64
Difference f minus m =  −0.06, 95% CI =  −0.59 to 0.48, t =  −0.22, p > 0.83 ns
LR8Ff 20 16.07 1.78 13.65 14.70 15.58 17.21 20.48
LR8Fm 37 15.49 1.24 12.94 14.48 15.62 16.60 17.59
Difference f minus m = 0.58, 95% CL =  −0.23 to1.39, t = 1.44, p > 0.155 ns
LR8Gf 37 17.67 1.97 14.36 16.25 17.10 19.26 21.37
LR8Gm 71 16.82 1.23 14.36 15.57 17.07 17.41 20.81
Difference f minus m = 0.58, 95% CL =  −0.24 to 1.46, t = 2.75, p > 0.01 ns
LL1Ff 5 7.35 0.51 6.86 6.99 7.25 7.52 8.15
LL1Fm 6 7.44 0.67 6.87 6.97 7.18 7.77 8.65
Difference f minus m =  −0.09, 95% CI =  −0.92 to 0.74, t =  −0.25, p > 0.81 ns
LL1Gf 13 7.86 0.63 7.13 7.40 7.78 8.24 9.42
LL1Gm 25 7.89 0.62 6.32 7.35 8.02 8.29 8.71
Difference f minus m =  −0.03, 95% CI =  −0.46 to 0.40, t =  −0.14, p > 0.89 ns
LL2Ff 13 7.68 0.71 6.86 7.15 7.44 8.13 9.42
LL2Fm 28 7.93 0.74 6.32 7.32 7.98 8.41 9.33
Difference f minus m =  −0.25, 95% CI =  −0.74 to 0.25, t =  −1.02, p > 0.31 ns
LL2Gf 33 8.48 0.81 7.36 7.93 8.38 8.81 10.38
LL2Gm 28 8.70 0.84 7.03 8.14 8.67 9.31 10.28
Difference f minus m =  −0.22, 95% CI =  −0.64 to 0.20, t =  −1.04, p > 0.30 ns
LL3Df
LL3Dm 1 8.05 8.05 8.05 8.05 8.05 8.05
Unable to compute
LL3Ef 14 7.63 0.53 6.86 7.15 7.58 8.15 8.38
LL3Em 33 7.96 0.70 6.32 7.35 7.95 8.47 9.33
Difference f minus m = 0.33, 95% CL =  −0.75 to 0.09, t =  −1.58, p > 0.12 ns
TDS n- tds x¯-tds sd-tds 0%ile (min) 25th%ile 50th%ile 75%ile 100%ile (max)
LL3Ff 87 9.08 1.01 7.22 8.29 8.94 9.86 11.42
LL3Fm 136 9.60 0.94 6.87 8.98 9.84 10.22 11.90
Difference f minus m =  −0.52, 95% CL =  −0.78 to -0.26, t =  −3.91, p > 0.00 ns
LL3Gf 163 11.61 0.81 8.63 10.08 10.48 11.24 12.92
LL3Gm 123 11.21 0.84 8.84 10.64 11.24 11.64 13.99
Difference f minus m = 5.4, 95% CL = 5.21 to 5.59, t = 54.93, p > 0.00 ns
LL4Df 1 7.22 7.22 7.22 7.22 7.22 7.22
LL4Dm 3 7.40 0.94 6.32 6.32 7.86 8.02 8.02
unable to compute
LL4Ef 43 8.14 0.74 6.86 7.47 8.21 8.53 10.26
LL4Em 57 8.32 0.78 6.87 7.86 8.28 8.72 10.07
Difference f minus m =  −0.18, 95% CL =  −0.49 to 0.13, t =  −1.17, p > 0.25 ns
LL4Ff 101 9.77 0.96 7.89 8.99 9.85 10.31 12.68
LL4Fm 122 9.98 0.92 7.31 9.26 10.00 10.66 11.90
Difference f minus m =  −0.21, 95% CL =  −0.46 to 0.04, t =  −1.66, p > 0.09 ns
LL4Gf 167 10.94 0.75 9.32 10.38 10.93 11.42 13.36
134 11.34 0.93 9.06 10.64 11.34 11.90 13.99
Difference f minus m =  −0.4, 95% CL =  −0.59 to −0.21, t =  −4.13, p > 0.00 ns
LL5Df 10 7.89 0.90 6.86 7.13 7.74 8.68 9.40
LL5Dm 20 7.82 0.67 6.32 7.25 7.94 8.28 9.08
Difference f minus m = 0.07, 95% CL =  −0.53 to 0.67, t = 0.24, p > 0.81 ns
LL5Ef 55 8.56 0.86 7.15 7.95 8.41 8.95 10.36
LL5Em 79 9.12 1.09 6.87 8.26 8.98 10.07 11.44
Difference f minus m =  −0.56, 95% CL =  −0.91 to −0.21, t =  = 3.18, p > 0.00 ns
LL5Ff 172 10.42 0.95 8.03 9.95 10.40 11.08 13.08
LL5Fm 169 10.65 1.05 8.52 9.98 10.64 11.32 14.05
Difference f minus m =  −0.23, 95% CL =  −0.44 to −0.02, t =  −2.12, p > 0.03 ns
LL5Gf 134 11.44 0.88 9.61 10.83 11.39 11.94 13.82
LL5Gm 104 12.06 1.04 9.18 11.49 12.09 12.74 13.99
Difference f minus m =  −0.62, 95% CL =  −0.87 to −0.37, t =  −4.98, p > 0.0 ns
LL6Ff 1 7.13 7.13 7.13 7.13 7.13 7.13
LL6Fm 2 8.84 0.77 8.29 8.29 8.84 9.39 9.39
Unable to compute
LL6Gf 35 7.99 0.68 6.86 7.47 8.11 8.30 10.38
LL6Gm 52 8.34 1.00 6.32 7.81 8.25 8.71 11.89
Difference f minus m =  −0.35, 95% CL =  −0.74 to 0.04, t =  −1.81, p > 0.07 ns
LL7Cf
LL7Cm 1 11.78 11.78 11.78 11.78 11.78 11.78
Unable to compute
TDS n- tds x¯-tds sd-tds 0%ile (min) 25th%ile 50th%ile 75%ile 100%ile (max)
LL7Df 26 7.86 0.65 6.86 7.37 7.71 8.29 9.40
LL7Dm 42 8.26 1.26 6.32 7.35 8.04 8.65 11.86
Difference f minus m =  −0.4, 95% CL =  −0.93 to 0.13, t =  −1.49, p > 0.14 ns
LL7Ef 77 9.46 1.09 7.36 8.56 9.42 10.26 12.13
LL7Em 89 9.56 0.94 7.34 8.76 9.66 10.18 11.65
Difference f minus m =  −0.1, 95% CL =  −0.41 to 0.21, t =  −0.63, p > 0.53 ns
LL7Ff 102 10.29 0.92 8.19 9.72 10.26 10.78 13.08
LL7Fm 94 10.52 0.96 8.58 9.98 10.40 11.27 12.76
Difference f minus m =  −0.23, 95% CL =  −0.49 to 0.03, t =  −4.98, p > 0.0 ns
LL7Gf 222 11.65 0.98 9.98 11.01 11.51 12.21 14.66
LL7Gm 226 12.27 1.16 9.18 11.44 12.19 13.14 15.86
Difference f minus m = 2.1, 95% CL = 1.89 to 2.3, t = 20.47, p > 0.0 ns
LL8Af 39 10.11 1.18 7.52 9.42 10.09 11.29 12.59
LL8Am 28 9.55 1.18 7.67 8.61 9.39 10.34 11.44
Difference f minus m = 0.56, 95% CL = −0.02 to 1.14, t = 1.92, p > 0.06 ns
LL8Bf 43 9.67 0.94 7.93 8.88 9.92 10.48 12.04
LL8Bm 33 10.39 1.31 8.12 9.57 10.00 11.18 13.43
Difference f minus m =  −0.72, 95% CL =  −1.23 to −0.21, t =  −2.79, p > 0.01 ns
LL8Cf 83 10.76 1.05 8.19 10.10 10.61 11.48 13.36
LL8Cm 111 10.98 1.35 8.52 10.06 10.67 11.90 15.02
Difference f minus m =  −0.22, 95% CL = −0.57 to 0.13, t = −1.23, p > 0.22 ns
LL8Df 172 12.01 1.18 9.17 11.56 11.90 12.79 15.33
LL8Dm 207 12.68 1.48 8.58 11.60 12.76 13.52 17.13
Difference f minus m =  −0.67, 95% CL = −0.94 to – 0.39, t =  −4.80, p > 0.0 ns
LL8Ef 37 14.39 1.56 11.97 13.51 14.13 14.94 18.39
LL8Em 80 14.26 1.35 11.40 13.45 13.84 15.21 18.64
Difference f minus m = 0.13, 95% CL = −0.43 to 0.69, t = 0.46, p > 0.65 ns
LL8Ff 26 15.90 1.72 12.93 14.81 15.50 16.95 19.52
LL8Fm 35 15.47 1.13 13.32 14.48 15.47 16.33 17.59
Difference f minus m = 0.43, 95% CL =  −0.30 to1.161, t = 1.178, p > 0.244 ns
LL8Gf 39 18.00 2.08 14.36 16.25 17.44 20.14 21.37
LL8Gm 68 16.84 1.22 14.36 15.96 17.10 17.43 20.81
Difference f minus m = 1.16, 95% CL = 0.53 to1.79, t = 3.64, p > 0.00 ns
TDS n- tds x¯-tds sd-tds 0%ile (min) 25th%ile 50th%ile 75%ile 100%ile (max)

Worked example

The detailed procedure for the computer-assisted procedure to estimate individual DA is detailed in a paper published in 2018 [6].

Validation study

The data extracted for each subject are illustrated in the worked example (Table 4). The VS of 100 subjects comprise 50 females and 50 males age between 5 and 25 years with known date of birth and date of radiographic exposure. The results from the calculations for the whole of the VS are shown separately for females (Fig. 4) and males (Fig. 5).

Table 4.

Worked example. Data extracted for subject 47. The Demirjian TDS are indicated as B, E, F, G, H. The data for these stages are extracted from the RDS (Table 3). AaA, age at assessment

Tooth (anatomical description) FDI notation Demirjian TDS AaA
x¯
AaA
sd
BrDJ notation
Upper left central incisor 21 H - - UL1
Upper left lateral incisor 22 G 9.53 1.01 UL2
Upper left canine 23 F 9.39 1.09 UL3
Upper left first premolar 24 F 9.57 0.85 UL4
Upper left second premolar 25 E 8.83 0.95 UL5
Upper left first permanent molar 26 H - - UL6
Upper left second permanent molar 27 E 9.56 1.16 UL7
Upper left third molar 28 - - - UL8
Lower left third molar 38 B 9.67 0.94 LL8
Lower left second permanent molar 37 F 10.29 0.92 LL7
Lower left first permanent molar 36 H - - LL6
Lower left second premolar 35 F 10.42 0.95 LL5
Lower left first premolar 34 F 9.77 0.96 LL4
Lower left canine 33 F 9.08 1.01 LL3
Lower left lateral incisor 32 H - - LL2
Lower left central incisor 31 H - - LL1

Dental age = 9.61 years; sd = 0.98; chronological age = 10.41 years

The mean CA for females was 10.8 years, sd 1.74 years, the lower confidence limit was 10.31 years, and the upper confidence limit was 11.29 years.

The mean difference between CA and estimated DA is 0.40 years or an overestimate of 4.84 months (Table 5).

Table 5.

The validation sample comparison of CA and DA for females

Variable [females] Observation x¯(years) sd Limits of 95% conf. interval
CAf 50 10.8 1.74 10.31 11.29
DAf 50 10.4 1.33 10.03 10.78
Difference 50 0.40 0.93 0.14 0.67
t 3.06
Degrees of freedom 49
p value 0.0036
Difference in months 4.84

The mean CA for males was 10.89 years, sd 1.82 years, the lower confidence limit was 10.37 years, and the upper confidence limit was 11.41 years.

The mean difference between CA and DA for males is an overestimate of 0.38 years or 4.55 months (Table 6).

Table 6.

The validation sample comparison of CA and DA for males

Variable [males] Observation x¯(years) sd Limits of 95% conf. interval
CAm 50 10.89 1.82 10.37 11.41
DAm 50 10.52 1.42 10.11 10.92
Difference 50 0.38 0.91 0.12 0.64
t 2.49
Degrees of freedom 49
p value 0.0050
Difference in months 4.55

Bland–Altman graphs

These graphs [7] explore the agreement between CA and DA for both female and male subjects. The graphs (Figs. 4 and 5) demonstrate the difference between the CA and DA within the female and male VS groups. The horizontal dotted line is located at the mean difference between the CA and DA whilst the continuous line represents the “no difference” value between CA and DA. In both the female and male groups, the mean difference was statistically significant.

Discussion

The aim of DAE is to estimate the chronological age of an individual with unknown date of birth. It may be used for clinical, legal, civil, criminal, and forensic purposes. An important consideration is the error of the method. The preferred term is the uncertainty associated with the estimate. This is easily and reliably achieved using Draft Quicksheets [6].

In the present study, only the tooth development stages, which comprise the first part of the Demirjian, Goldstein, and Tanner method [2], were employed. This is because the mathematical procedures leading to the maturity score cannot be unravelled [8]. In the SAM procedure, all the developing teeth on the left side including third molars were used. This has increased the number of assessed teeth and therefore likely to lead to an improvement in the accuracy or reliability of the estimated age as it uses the maximum information available. This is born out to a considerable extent by the relatively small CA minus DA differences shown in the results of the validation study. In addition, the inclusion of third molars up to and including stage G makes the method suitable for older subjects aged 16 years or more. Stage H is not used as it is unbounded in its upper border for each tooth. This will be the subject of a further communication because the age of apex closure cannot be identified without censoring [8]. Also, the mean value for stage H cannot be used in simple mathematical procedures such as averaging the AaA of TDSs present in a subject. Therefore stage H is excluded from any calculations in SAM.

Each ethnicity or ancestral group has its own characteristics including with DAE. Many studies investigate the validity of using the French-Canadian standards on other populations [911]. However, it was found that overestimation or underestimation of age was present in many studies. From those findings developed the concept of establishing ethnic specific RDSs which is important for each specific Identifiable Human Group or Ancestral Group.

In this study, a RDS with the mean AaA of different TDS for the Qatari population was developed. The mean age at attainment is the term often used in DAE research reports. This is logically inconsistent as the age at which a stage has been attained cannot be ascertained from a single “snapshot” radiograph of developing teeth. Age at assessment (AaA) is the logical and better understood term.

The data collection was limited to Qatari subjects and partitioned by sex. The large number of DPTs results in a sufficiently large number in each 1 year age span and consequently for the individual TDS to ensure a narrow range of the CI of the differences between females and males. In practice, over 73% of the AaA values for the individual TDS were earlier in females. This emphasizes the importance of sex-specific data in addition to the need for ethnic specific data.

This work is part of a large dental age estimation project looking at different ancestral or identifiable human groups [6, 12, 13]. The main purpose of the study was to establish a RDS for the Qatari population. This was achieved using the 8 stage system of TDSs. A strong justification for this is that it has been shown to return the highest WOA and BOA using Cohen’s kappa. The scheme is practical, reliable, and easy to use as has been shown by the German research team [14]. Other publications using SAM have shown consistent accuracy within 6 months of the CA [13, 15].

The second part of the Demirjian, Goldstein, and Tanner method [2] has not been used as it has been suggested that age estimates based on the French-Canadian population are not applicable to other populations [2]. The method used here has been named as the simple average method (SAM) as it uses all the tooth morphology types without any idiosyncratic weighting, and on logical grounds, it is intuitively more informative to use as much information by using all tooth morphology types available [6].

A simple assessment to emphasize the need for ancestral specific data is to compare two TDS at random: Kuwait LL7Gf cf Qatar LL7Gf. These two stages from different Middle Eastern populations provide data as follows – Kuwait LL7Gf n-tds = 95, x-tds = 13.56, sd-tds = 1.55; Qatar LL7Gf n-tds = 222, x-tds = 11.65, sd-tds = 0.98. The difference of 1.91 years is highly statistically different. Whether this applies to the other TDSs in these RDSs is a matter for future investigation and report. This raises the important question of the suitability of using apparently similar genetic backgrounds for DAE.

The robust nature of the present study has been supported by the following:

  1. A single identifiable human group – Qatari subjects – has been used to create the reference data set.

  2. Training and careful management of the WOA and BOA supports the use of the 8 stage TDS system.

  3. A validation set was used to test the accuracy of the RDS when used to estimate the dental age of Qatari children, adolescents, and young adults.

Conclusion

A reference data set for the mean age at assessment of the different TDS for the Qatari population has been established. The average difference between CA and DA for female and male subjects was 4.8 months and 4.5 months, respectively.

Key points

  1. A large reference data set has been established for Qatari subjects.

  2. There is a clear difference between Age at Assessment for females and males as regards Tooth Development Stages.

  3. The Simple Average Method provides age estimation within 5 months of the Chronological Age.

Author contribution

All authors took part in designing the study. NAO and NAH acquired the data. NAO, VSL, and GR analysed the data. All authors contributed to the drafting and final version of the paper.

Data availability

All primary data is available from the corresponding author.

Declarations

Ethical approval

Ethical approval was granted by the Hamad Medical Corporation in Qatar (Research Centre).

Consent to participation

Not applicable as existing radiographs were re-used.

Consent for publication

Not applicable.

Conflict of interest

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.United Nations Convention on the Rights of the Child. 1989. www.unicef.org/child-rights-convention/convention-text-children’s-version
  • 2.Demirjian A, Goldstein H, Tanner JM. A new system of dental age assessment. Hum Biol. 1973;45:221–227. [PubMed] [Google Scholar]
  • 3.Cameriere R, Ferrante L, Liversidge HM, Prieto JL, Brkic H. Accuracy of age estimation in children using radiographs of developing teeth. Forensic Sci Int. 2008;176:173–177. doi: 10.1016/j.forsciint.2007.09.001. [DOI] [PubMed] [Google Scholar]
  • 4.Cohen J. A Coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20:37–46. doi: 10.1177/001316446002000104. [DOI] [Google Scholar]
  • 5.Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–174. doi: 10.2307/2529310. [DOI] [PubMed] [Google Scholar]
  • 6.Draft D, Lucas VS, McDonald F, Andiappan M, Roberts G. Expressing uncertainty in dental age estimation: a comparison between two methods of calculating the ‘average’ standard deviation. J Forensic Sci. 2019;64(5):1506–1509. doi: 10.1111/1556-409.14049. [DOI] [PubMed] [Google Scholar]
  • 7.Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;307–10. 10.1016/S0140-6736(86)90837-8. [PubMed]
  • 8.Roberts G, Lucas VS. Is the Demirjian, Goldstein and Tanner method of dental age assessment obsolete? A critical review and re-assessment. Insights Anthropol. 2012;5(1):325–36.. [Google Scholar]
  • 9.Nykanen R, Espeland L, Kvaal SI, Krogstad O. Validity of the Demirjian method for dental age estimation when applied to Norwegian children. Acta Odontol Scand. 1998;56:238–244. doi: 10.1080/00016359850142862. [DOI] [PubMed] [Google Scholar]
  • 10.Koshy S, Tandon S. Dental age assessment: the applicability of Demirjian’s method in South Indian children. Forensic Sci Int. 1998;94(1–2):73–85. doi: 10.1016/A0379-0738(98)0034-6. [DOI] [PubMed] [Google Scholar]
  • 11.Ambarkova M, Galic I, Vodanovic M, Biocina-Lukenda D, Brkic H. Dental age estimation using Demirjian and Willems methods: cross sectional study on children from the former Yugoslavia Republic of Macedonia. Forensic Sci Int. 2014;234(187):e1–e187. doi: 10.1016/j.forsciint.2013.10.024. [DOI] [PubMed] [Google Scholar]
  • 12.Moze K, Roberts GJ. Dental age assessment (DAA) of Afro-Trinidadian children and adolescents: development of a reference data set and comparison with Caucasians resident in London. UK J Leg and Forens Med. 2012;19:272–279. doi: 10.1016/j.jflm.2011.12.033. [DOI] [PubMed] [Google Scholar]
  • 13.Karimi A, Qudeimat MA, Lucas VS, Roberts G. Dental age estimation: development and validation of a reference data set for Kuwaiti children. Arch Oral Biol. 2021;127:105130. doi: 10.1016/j.archoralbio.2021.105130. [DOI] [PubMed] [Google Scholar]
  • 14.Olze A, Bilang D, Schmidt S, Wernecke KD, Geserick G, Schmeling A. Validation of common classification systems for assessing the mineralization of third molars. Int J Leg Med. 2005;119:22–26. doi: 10.1007/s00414-004-0489-. [DOI] [PubMed] [Google Scholar]
  • 15.Mitchel JC, Lucas VS, Roberts GJ. Dental age assessment: reference data for children at the 16 year threshold. Forens Sci Int. 2009;189:19–23. doi: 10.1016/j.forsciint.2009.04.002. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All primary data is available from the corresponding author.


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