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Dental age estimation in adults
Chapter 9
Dental age estimation in adults Stella Martin de las Heras Forensic Medicine and Forensic Dentistry Department, School of Medicine, Granada, Spain
9.1 Introduction Age estimation in adults is a complex issue for forensic scientists because the aging process is influenced by numerous factors, including lifestyle, nutrition, type of work, exercise, toxic habits, diseases, and treatments. Age estimation in the unidentified dead can contribute to a biological profile for comparison with data on missing persons, while age estimation in living adults without valid identification documents plays an important role in criminal and civil proceedings [1]. The demand for age estimates of living persons has markedly increased over recent years due to a major rise in the migration of populations [2]. Dental age estimation techniques are considered to be highly reliable in children but less accurate in adults; however, because the age interval for adults is much wider, age estimation errors of 6 710 years can be acceptable in adults but not in children. Nevertheless, age assessment is more difficult in adults, and there is no consensus on the most appropriate technique. When all permanent teeth have been formed, age can no longer be estimated by studying their development, although some changes in dental tissues and related structures are produced by the normal aging process. Biochemical and morphological methods have been used to evaluate degenerative processes undergone by teeth over time. Morphological methods are the most widely used in practical forensic cases and include the study of secondary dentin formation, root translucency, cement apposition, and dentin color, among others. Biochemical studies are more complex and involve destruction of the teeth [3]. The choice of dental age estimation techniques depends on the circumstances. Thus, destructive techniques, tissue sampling, and radiological exposure are always acceptable in dead but not living adults, while even low-dose exposure is considered inappropriate for age determination in some countries and other ethically viable approaches must be applied [4,5].
Age Estimation. DOI: https://doi.org/10.1016/B978-0-12-814491-6.00009-1 Copyright © 2019 Elsevier Inc. All rights reserved.
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9.2 Morphological dental age-related changes The most frequently studied age-related morphological changes in teeth are dental attrition or wear, tooth color, periodontal recession, secondary dentin deposition, dentin transparency, root resorption, root surface roughness, and cementum apposition. G
G
G
G
Attrition. This refers to the reduction in dental tissue by contact between antagonist teeth, causing wear facets on occlusal aspects or incisal margins. Given that attrition can also be caused by factors other than age, including diet, habits/customs, diseases (e.g., bruxism), and type of occlusion, among others, it is accepted for age estimation only in combination with other parameters [6]. Tooth color. Age-related changes in enamel color are caused by increased nitrogen content and surface cracking, which leads to light refraction changes. Changes in dentin color are attributable to modifications in mineral and organic composition. The root is less frequently affected by pathological processes in comparison to the crown (e.g., caries or staining), which is more exposed to external factors that alter tooth color. Hence, color should be measured whenever possible in the proximal third of root dentin after elimination of the cementum, which involves tooth extraction [7,8]. Color can be measured on the enamel in living individuals, but the greater exposure of this tissue to external factors should be taken into account. Periodontal recession. The original term used by Gustafson in his original [9] and subsequent studies was Periodontosis, but this designation is now considered obsolete. Older age is accompanied by the destruction of periodontal fibers at the cervical margin, progressing toward the apex, and periodontal recession is defined as the maximum distance between the cementoenamel junction and the soft-tissue attachment line (Fig. 9.1) and is measured on the vestibular aspect [10]. However, recession not directly related to age can be observed in acute or chronic inflammatory processes such as periodontal disease and can even result from periodontal treatment. A further limitation of its use to estimate age is that the soft-tissue attachment line is often not visible in skeletonized remains. Secondary dentin. Secondary dentin is produced after complete tooth formation and reduces the pulp cavity size. It commences in the coronal region of the pulp, where contact is made with the antagonist tooth during mastication. When coronal pulp filling is complete, secondary dentin formation continues through the root canal or canals. This change can be measured by direct observation of the sectioned tooth under binocular loupes [11] or by indirect X-ray observation to calculate the pulp size [1216]. Both secondary dentin quantification and pulp cavity reduction can be used as age estimation methods in forensic studies.
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FIGURE 9.1 Image of tooth showing the attachment line of periodontal soft tissues. G
G
Root transparency. This phenomenon is caused by an increase in peritubular dentin deposits, which changes the refractive index of root dentin. It commences in the dentin closest to the apex and extends toward the crown over the years (Fig. 9.2). Root transparency is considered one of the factors most closely associated with age and has been recommended as a single indicator for age calculation [10,1719]. Among the different root translucency measurements considered (length, area, width), the most widely used is the maximum length of the translucent area from apex to crown, usually on the distal aspect of the tooth. Root resorption and increased root surface roughness. The root surface of teeth becomes rougher and more irregular over the years. Root roughness measurement under binocular loupe is combined with assessment of the defects produced by root resorption, which can also be affected by orthodontic treatments, periapical disease, and dental trauma. However, root resorption alone has shown a low correlation with age and should only be considered in combination with other dental changes for age estimation purposes [20].
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FIGURE 9.2 Detailed image of the apical area of sectioned tooth, showing dentin transparency.
G
Cementum apposition. Apposition of cementum on the tooth root is observed with older age. Acellular cementum is observed in the coronal half of the root but a mixture of cellular and acellular cementum predominates in the apical half. Cementum apposition varies widely among different root localizations, being maximum in the apex and minimum close to the cementoenamel junction. It has been reported to augment with older age, with findings of a threefold increase in the cementum layer between the ages of 11 and 70 years [21]. In most mammals, including humans, cementum apposition takes place in phases, resulting in two types of layer with distinct color and optical properties. Narrow dark-stained lines (incremental lines) are separated by wide cementum bands and the count of incremental lines has been used as an age estimation method, although it has serious limitations [22,23].
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9.3 Morphological dental age estimation methods The morphological changes undergone by teeth over time have been used to develop numerous predictive models for age estimation. We have selected here the most important and practically useful methods. The first scientific method based on tooth sections was published in 1950 by Gustafson [9], who macroscopically assessed six age-related parameters: attrition (A), secondary dentin formation (S), periodontal recession (P), cementum apposition (C), root resorption (R), and root transparency (T), with each parameter scored on a 4-point scale (03 points). Using the sum of the scores, he obtained a regression line for age estimation. Applying the formula Age 5 11.43 1 4.56x, he estimated age with an approximate error of 6 78 years, although this result has not been reproduced in subsequent studies [24,25]. In general, it is now accepted that the mean error of Gustafson’s method is 6 1015 years. Gustafson’s method [9] had a major impact in the field of dental age estimation in adults and formed the basis for the development of new techniques that take account of additional parameters (e.g., tooth type, sex, and/or ancestry). In 1971, Johanson [26] studied the same six age-related structural changes as Gustafson [9] but applied a 7-point (grade) scale (Fig. 9.3). He used multiple regression analysis to establish a general formula in which different contributions are made by the distinct dental parameters as follows: Age 5 11.02 1 5.14A 1 2.3S 1 4.14P 1 3.71C 1 5.57R 1 8.98T. The error of this method was 6 5.2 years, considering a standard deviation of 6 10 years with two standard deviations for the analysis of multiple teeth or of 6 16 years with two standard deviations for the analysis of a single tooth. Johanson took no account of the type of tooth or the sex of the individual in his method.
FIGURE 9.3 Drawing showing the seven different grades used for the six parameters analyzed in the Johanson’s method. Johanson G. Age determination from human teeth. Odont Revy 1971;22(21):40126.
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Lamendin [10] developed an age estimation method considering root height, periodontal recession, and root transparency. Age 5 (0.18 3 P) 1 (0.42 3 T) 1 25.53, where P is defined as P 5 (measured periodontal recession height 3 100)/measured root height and T 5 (measured root transparence height 3 100)/measured root height. This method should not be applied to individuals below 40 years due to inaccuracy shown in younger individuals. Bang and Ramm [27] developed an age estimation method that considered root dentin transparency alone, being the parameter least influenced by external factors, sex, or ancestry. Root transparency length (mm) is measured from the apex in intact or sectioned teeth, which should be cut in a vestibularlingual direction, preserving the dental structure in the center of the pulp and showing the apex. If the area of translucency is not straight, the mean of the maximum and minimum measurements is taken. In the Bang and Ramm method, age is estimated from one formula if the root translucency length is # 9 mm and from another if it is .9 mm, because of a slowing in dentin production after the age of 70 years. Both formulas also take into consideration the type of tooth (Table 9.1). However, one shortcoming of this method is that the length of translucent dentin is expressed in absolute values and is not related to the total length of the root. In 1978, Maples [28] developed age estimation formulas for different types of teeth that considered secondary dentin (S) and root translucency (T) alone, using the 7-point scale of Johanson. They achieved highly acceptable results, with estimation errors ranging between 6 6.8 and 6 12.2 years according to the type of tooth under study (Table 9.2). Solheim [29] proposed a formula for each type of single-rooted tooth (upper and lower), selecting parameters that showed the strongest correlation with age. He also developed another multiple regression model for age estimation in which tooth color and sex were included as independent variables. Thus, for the first upper premolar, age estimation is based on the formula: Age 5 8 1 7.3CEST 1 4.1SJ 1 1.4TID; where CEST is the color of the tooth estimated with a color guide; SJ, secondary dentin formation according to the Johanson method; and TID, the length of dentin transparency (mm) in the intact tooth. The standard error of this method ranges between 6 7.0 and 6 12.9 years according to the tooth type. In the case of the formula for the first upper premolar cited above, the error is 6 10.8 years (Table 9.3). Application of Solheim’s method [29] usually requires the utilization of handpiece and drill to obtain a mesial to distal vestibulolingual section up to the middle pulp area (Fig. 9.4). This “half-tooth” technique, first described in 1984 [30], avoids the loss of apex with curvature that limited previously used thin-slice techniques. After preparation of the tooth, the different measurements are made under a stereomicroscope (magnifying glass) with the assistance of a planigraph.
TABLE 9.1 Data for age determination and standard deviation (SD) in years according to the Bang and Ramm’s method [27] # 9 mm Intact roots
.9 m Sectioned roots
Intact roots
Sectioned roots
Type of tooth
B0
B1
B2
SD
B0
B1
B2
SD
B0
B
SD
B0
B
SD
Upper right central incisor
20.3
5.74
0.000
10.42
21.02
6.03
2 0.060
11.43
20.34
5.74
10.42
22.36
5.39
11.25
Upper left central incisor
24.3
6.22
2 0.119
9.71
26.84
6.00
2 0.155
11.03
26.78
4.96
9.58
30.18
4.30
10.93
Upper right lateral incisor
18.8
7.10
2 0.164
10.83
23.09
7.04
2 0.197
11.25
22.06
5.36
10.73
25.55
5.23
11.20
Upper left lateral incisor
20.9
6.85
2 0.223
9.77
24.62
5.18
2 0.077
9.40
25.57
4.38
9.81
25.90
4.39
9.24
Upper right cuspid
26.2
4.64
2 0.044
12.59
21.52
6.49
2 0.171
13.29
28.13
4.01
12.39
28.01
4.23
13.12
Upper left cuspid
25.27
4.58
2 0.073
13.80
24.64
5.22
2 0.143
13.78
27.59
3.65
13.60
29.41
3.32
13.70
Upper first premolars
23.91
3.02
0.203
11.62
29.98
2.73
0.107
13.17
18.42
5.40
11.41
28.44
3.81
12.93
Upper right second premolar
23.78
5.06
2 0.064
11.21
24.76
4.81
0.000
10.43
25.33
4.28
10.96
24.75
4.81
10.43
Upper left second premolar
25.95
4.07
2 0.067
9.48
22.34
7.59
2 0.393
6.88
26.92
3.37
9.32
26.21
4.03
7.62
(Continued )
TABLE 9.1 (Continued) # 9 mm Intact roots
.9 m Sectioned roots
Intact roots
Sectioned roots
Lower right central incisor
9.80
12.61
2 0.711
10.91
13.63
12.11
2 0.683
11.01
29.00
4.23
11.85
31.78
4.19
12.09
Lower left central incisor
23.16
9.32
2 0.539
12.27
26.46
8.79
2 0.511
11.74
37.56
2.94
12.84
37.89
3.08
12.31
Lower right lateral incisor
26.57
7.81
2 0.383
11.95
21.77
10.19
2 0.581
11.26
38.81
2.81
12.43
38.49
3.03
12.42
Lower left lateral incisor
18.58
10.25
2 0.538
10.08
22.22
9.07
2 0.444
10.78
33.65
3.53
11.12
35.19
3.49
11.46
Lower right cuspid
23.30
8.45
2 0.348
10.52
24.34
8.38
2 0.358
10.74
37.80
3.50
11.24
40.32
3.05
11.77
Lower left cuspid
27.45
7.38
2 0.289
10.22
23.88
8.76
2 0.388
9.96
41.50
2.84
10.74
42.07
2.73
10.88
Lower right first premolar
24.83
6.85
2 0.237
9.53
21.54
8.63
2 0.395
10.19
30.83
4.05
9.73
33.10
3.66
11.08
Lower left first premolar
29.17
5.96
2 0.173
10.13
26.02
7.00
2 0.234
8.39
34.97
3.74
10.14
32.79
4.11
8.71
Lower right second premolar
29.42
4.49
2 0.065
13.00
14.90
9.93
2 0.451
9.05
30.68
3.76
12.82
27.46
4.17
11.23
Lower left second premolar
18.72
5.79
2 0.082
11.52
23.87
5.50
2 0.098
10.85
20.87
4.79
11.38
25.60
4.41
10.77
Upper 6 molars, mesial root
30.25
3.23
2 0.018
10.22
28.22
4.82
2 0.101
6.85
30.56
3.00
10.08
30.03
3.48
7.11
Lower 6 molars, mesial root
27.39
6.25
2 0.239
10.49
33.42
5.18
2 0.302
15.76
30.32
3.66
11.04
35.27
2.78
15.74
Upper 6 molars, distal root
34.73
0.67
0.211
10.68
20.43
6.09
2 0.182
10.85
29.49
3.32
11.09
26.89
3.55
10.92
Lower 6 molars, distal root
30.21
5.52
2 0.181
10.69
29.91
4.97
2 0.102
11.10
31.46
3.77
10.89
30.31
4.22
11.00
Upper 6 molars, palatinal root
27.43
3.64
0.039
10.02
25.15
4.34
2 0.032
10.20
26.81
4.07
9.98
25.83
3.95
10.16
When the translucent dentin length (X) is # 9 mm, the formula applied is AGE 5 B0 1 B1X 1 B2X2. When the translucent dentin length (X) is .9 mm, the formula is AGE 5 B0 1 BX. Source: Bang G, Ramm E. Determination of age in humans from root dentin transparency. Acta Odontol Scand 1970;28(1):335.
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TABLE 9.2 Formulas for age calculation in different types of teeth and the standard error (SE) in years according to the Maples method [28] Formula
SE (years)
Central incisor
3.89S 1 14.23T 1 15.28
9.1
Lateral incisor
6.51S 1 12.55T 1 25.26
9.6
Cuspid
18.67S 1 11.72T 1 21.94
11.0
First bicuspid
2.82S 1 15.25T 1 19.65
12.2
Second bicuspid
4.79S 1 15.53T 1 17.99
7.6
First molar
11.28S 1 5.32T 1 10.86
11.1
Second molar
6.99S 1 10.86T 1 19.31
6.8
Third molar
4.71S 1 12.30S 1 24.57
12.0
S, Secondary dentin as Johanson’s method; T, root transparency as Johanson’s method. Source: Maples WR. Improved technique using dental histology for estimation of adult age. J Forensic Sci 1978;23(4):76470.
Over the past few years, methods based on the above parameters have been developed using technologies that avoid examiner subjectivity, such as automatized image analysis. In this line, Mandojana et al. [31] proposed formulas to determine age in worn teeth (according to the half-tooth technique). They measured the dental parameters in the usual manner and also automatically (e.g., proportional pulp cavity size and root dentin transparency), digitalizing the half-tooth image and applying the Visilog 5 morphometric analysis program (Noesis S.A., Quebec, Canada). The standard error for age estimation by this method was 6 9.9 years [31,32]. More recently, authors [18] used image analysis techniques for the quantification of dentin transparency alone, acquiring a digital image of the extracted (intact) tooth and using the Photoshop CS4 program (Adobe systems Inc., San Jose, CA, USA) to calculate the mean luminance value (cd/m2) of the translucent dentin area. They obtained an age estimation error ranging between 6 6.26 and 6 7.69 years, although the authors stipulated that new calibrations are required if a different camera is used. Tooth color has been found to make a significant contribution to age estimation based on dental morphological changes and is included in 7 out of the 10 formulas proposed by Solheim for this purpose [29]. Tooth color estimation by visual comparison with a known standard (tooth shade guide) has been widely utilized by forensic odontologists, but the capacity to distinguish colors varies among observers [7,8,29]. Nevertheless, despite the shortcomings of this subjective approach, little attention appears to have been paid to the development of improved dental color measurement
TABLE 9.3 Formulas for age estimation in different types of tooth and the error (standard error of estimates) in years, according to Solheim’s method [29] Formula
R2
SES
1
AGE 5 24.3 1 8.7CEST 1 5.2TD 2 2.3CAP 2 4.3SEX
0.83
7.0
2
AGE 5 38.7 2 126ST 1 4.7CEST 1 4.2TD 1 0.05C1
0.82
8.0
3
AGE 5 10.1 1 2.3TID 1 4.4SJ 1 6.1CEST
0.77
8.4
4
AGE 5 8.0 1 7.3CEST 1 4.1SJ 1 1.4TID
0.69
10.8
5
AGE 5 6.1 1 9.1CEST 1 3.3AJ 1 7.3LPMEAN 1 1.4TID
0.79
7.9
1
AGE 5 221.8 2 55.3SC 1 32.8LC1 2 10.3SEX 1 2.6TID
0.76
9.5
Tooth Maxilar
Mandibular
2
AGE 5 224.5 1 4.9CEST 1 2.1TID 2 7.0SEX 1 20.1LC1 1 2.4AJ
0.73
9.2
3
AGE 5 19.2 1 1.7TID 1 5.1CEST 1 3.5SJ
0.66
10.8
4
AGE 5 228.1 1 3.0TID 1 0.6ARA 1 24.1LC1 2 5.6SEX 1 7.3LPMEAN
0.77
8.7
5
AGE 5 7.5 1 2.7TID 1 4.9SJ 1 4.9SRS
0.58
12.9
Teeth: 1: central incisor; 2: lateral incisor; 3: canine; 4: first premolar; 5: second premolar. SES, Standard error of estimates; CEST, color estimate score; TD, translucency scored according to Dalitz; CAP, crown pulp area in square mm; ST, sum of pulp diameters/sum of root diameters; C1, sum of cementum thichness on vestibular and lingual surfaces; TID, length in mm of translucent zone in dry intact tooth; SJ, secondary dentin scored according to Johanson; AJ, attrition scored according to Johanson; LPMEAN, logarithmic transformation of periodontal recession; SC, pulp diameter/root diameter at the cervical area; LC1, logarithmic transformation of C1 (base 10); ARA, area of attrition in square mm; SRS, surface roughness score. Source: Solheim T. A new method for dental age estimation in adults. Forensic Sci Int 1993;59(2):13747.
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SECTION | II Odontological approach of age estimation
FIGURE 9.4 Photograph of a worn tooth in accordance with the half-tooth technique of Solheim [30].
techniques for age estimation [33,34]. A spectrophotometer measures the color of an observed object by reflection or transmission and gives the entire spectral curve, being limited to the visible frequency range (usually 350800 nm). Spectrophotometry quantifies color by chromaticity coordinates (x, y, z) or colorimetric variables (L , a , b ) and permits the calculation of whiteness (WI) or yellowness indexes (YI) [35,36]. The advantage of spectrophotometry is that it provides sufficient information to calculate color values for any illuminant and automatically detects metamerism, that is, color changes under different lighting and at different angles. Age-related color changes can be measured in both dentin and enamel. Dentin is more isolated from external factors and is therefore a better
Dental age estimation in adults Chapter | 9
137
candidate to determine age but this is an invasive method that can only be applied to cadavers. Dentin color has been used for age estimation alone and in combination with other age-related factors [8,29,33]. Enamel color may be useful in living individuals, especially in countries that consider even low-dose exposure to radiological imaging to be inappropriate for age determination. Spectrophotometry studies have demonstrated that enamel becomes less white and more yellow over the years [37], and receiver operating characteristic (ROC) analysis can be used to estimate the likelihood of an individual being over a certain age based on enamel color (Table 9.4). An area under the ROC curve (AUC) above 0.7 was achieved for enamel color measurements by spectrophotometry, indicating that it may be a useful method for forensic purposes, despite the potential effects on enamel of numerous external factors. [37]. For instance, when the WI of an upper incisor is ,5.92, an age .30 years can be estimated (AUC 5 0.8) with 75% sensitivity and 92% specificity, and when its YI is .28.59, an age .75 years can be estimated (AUC 5 0.7) with 91% sensitivity and 70% specificity. Age estimation radiographic methods that measure pulp cavity narrowing due to secondary dentin formation are noninvasive and can therefore be applied in living individuals. Cameriere et al. [15,16] used digitalized panoramic X-ray to calculate pulp and tooth area proportions using Photoshop CS4 software (Adobe systems Inc., San Jose, CA, USA). In this technique, the operator selects the total tooth area, marking a minimum of 20 points on the contour with a polygonal lasso tool. The pulp area is then selected, using a minimum of 10 points, and the program automatically calculates the value in pixels of each area. The author proposed age estimation formulas for different tooth types alone and in combination. For instance, the regression equation for the combination of teeth 34 and 35 (FDI notation) is as follows: Age ðyearsÞ 5 93:55 ð360:43 3 R34 Þ 2 ð380:69 3 R35 Þ 1 ð1855:3 3 R34 3 R35 Þ where R is the ratio of pulp to tooth area in pixels. The error (standard deviation) for this formula was 6 6.29 years. There is current research interest in the use of cone beam computed tomography images for the volumetric measurement of pulp chamber narrowing as an age estimation method [3840]. Three-dimensional techniques are expected to provide a more precise calculation in comparison to two-dimensional methods, but further research is warranted in larger samples to demonstrate their usefulness.
9.3.1
Age-related biochemical changes for dental age estimation
Tooth dentin consists of approximately 91% collagen and is an excellent substrate, being protected from many extrinsic environmental factors and being stable, except for small amounts of secondary and tertiary dentin
TABLE 9.4 Receiver operating characteristic (ROC) analysis for age estimation with colorimetric variables (L , a , b ), chromaticity coordinates (x, y, z), or whiteness (WIC, Z%, WI) and yellowness (YI) indexes measured by spectrophotometry in the enamel of upper incisors $ 30 years AUC
a
Youden
b
$ 40 years c
Se
Sp
d
AUC
a
Youden
b
Se
$ 50 years c
Sp
d
AUC
a
Youden
b
Se
$ 65 years c
Sp
d
AUC
a
Youden
b
Se
$ 70 years c
Sp
d
AUC
a
Youden
b
Se
$ 75 years c
Sp
d
AUC
a
Youdenb Sec Spd
L
0.6
60.74
77
57
0.5
60.74
78
42
0.5
49.81
95
18
0.5
50.92
94
19
0.5
59.23
73
41
0.5
52.04
91
20
a
0.8
0.36
87
71
0.7
0.92
66
67
0.7
0.27
90
38
0.7
0.94
78
50
0.7
0.78
93
46
0.7
2.69
64
74
b
0.8
9.21
59
85
0.7
9.21
66
78
0.8
9.22
75
72
0.7
10.09
78
70
0.8
10.43
86
76
0.8
10.44
91
74
x
0.8
0.335
78
85
0.7
0.336
80
64
0.7
0.335
88
52
0.7
0.342
78
66
0.8
0.340
93
53
0.8
0.344
91
51
y
0.7
0.351
56
92
0.7
0.351
65
85
0.7
0.353
70
79
0.7
0.353
78
59
0.7
0.354
80
71
0.7
0.354
82
69
z
0.8
0.309
71
92
0.7
0.304
70
75
0.8
0.301
72
72
0.7
0.300
78
60
0.8
0.301
93
58
0.8
0.298
82
65
WIC 0.8
2 34.28
74
92
0.8
2 34.28
78
67
0.8
2 37.33
81
63
0.7
2 42.35
72
60
0.8
2 38.02
93
54
0.8
2 42.35
91
60
Z%
0.7
25.36
87
57
0.6
25.36
91
42
0.6
25.36
93
31
0.6
23.15
79
30
0.6
19.68
73
54
0.6
22.27
81
36
WI
0.8
5.92
75
92
0.8
4.41
71
75
0.8
4.40
79
68
0.7
2.76
78
67
0.8
2.73
86
67
0.8
2.76
91
65
YI
0.8
25.77
68
92
0.8
26.47
68
78
0.8
25.22
86
61
0.7
28.25
78
70
0.8
28.54
86
72
0.8
28.59
91
70
a
AUC: Area under the ROC curve. Youden: Cutoff point that maximizes the sum of sensitivity and specificity. Se: Sensitivity. d Sp: Specificity. Source: Martin-de-las-Heras S D-RM, Molina A, Rubio L. Spectrophotometric dental colour measurement to assess age in living adults. Aust J Forensic Sci 2016;50:829. b c
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formation. Age-related biochemical changes in dentin include an increase in double collagen bonds, specifically deoxypyridinoline [41]; a decrease in gelatinases, enzymes responsible for degrading type I collagen [42]; a shortening of dental pulp DNA telomeres [43]; and the aspartic acid racemization [44,45]. This latter research line has strengthened over the past few decades, demonstrating that aging is accompanied by a change in amino acid spatial conformation (racemization), with an increase in dextrorotatory (D) over levorotatory (L) forms. All amino acids that form the proteins of the organism belong to stereochemical series L, with optical activity, given the presence of at least one asymmetrical carbon atom in their molecule. D forms progressively increase over the years until an equimolar mixture of D and L enantiomers is reached, constituting an inactive optical form. Among all amino acids, aspartic acid has the highest racemization reaction rate, which occurs as a first-order chemical reaction, indicating a constant rate with higher age. D-aspartic acid readily accumulates in tissues that metabolize slowly, such as the intervertebral disc, lens, and teeth. Racemization can be affected by three environmental factors: body pH, humidity, and temperature. Body temperature can produce the greatest modification in racemization rates, which increase at higher temperatures. Nevertheless, aspartic acid racemization in dentin is the most frequently used biochemical technique for age estimation and yields precise results in adults, with an error of approximately 6 3 years [44,45]. However, biochemical studies are methodologically complex (requiring special laboratory facilities and experience), time-consuming, and costly, and they involve destruction of the teeth [3]. For these reasons, morphological methods continue being the most widely employed in practical forensic cases.
9.4 Conclusions Age estimation in adults is required for the identification of skeletonized remains, as in major catastrophes, and of living humans, especially in the context of the increasing migration of populations. This task continues to pose a challenge to forensic scientists because aging is a particularly individual process. This chapter has addressed different research approaches, focusing on techniques that represent an important advance in this field and are of practical usefulness. Selection of the most appropriate method depends upon the characteristics of each case, and practitioners need to be aware of the range of options available, including their validity, reliability, and measurement error. Dental age determination by invasive techniques, tissue sampling, and/or X-ray examination is highly controversial in living individuals. Recent technological advances have allowed age-related dental changes to be measured in a more objective manner. Forensic odontologists play a key role in the age determination and identification of children and adults, and there is a need for continuing research to increase the reliability and value of their evidence.
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References [1] Cunha E, Baccino E, Martrille L, Ramsthaler F, Prieto J, Schuliar Y, et al. The problem of aging human remains and living individuals: a review. Forensic Sci Int 2009;193(1-3):113. [2] UNHCR. Global trends: refugees, asylum-seekers, returnees, internally displaced and stateless persons, ,http://www.unhcr.org/4c11f0be9.html.; 2009. [3] Soomer H, Ranta H, Lincoln MJ, Penttila A, Leibur E. Reliability and validity of eight dental age estimation methods for adults. J Forensic Sci 2003;48(1):14952. [4] Focardi M, Pinchi V, De Luca F, Norelli GA. Age estimation for forensic purposes in Italy: ethical issues. Int J Legal Med 2014;128(3):51522. [5] Schmeling A, Grundmann C, Fuhrmann A, Kaatsch HJ, Knell B, Ramsthaler F, et al. Criteria for age estimation in living individuals. Int J Legal Med 2008;122(6):45760. [6] Solheim T. Dental attrition as an indicator of age. Gerodontics 1988;4(6):299304. [7] Ten Cate AR, Thompson GW, Dickinson JB, Hunter HA. The estimation of age of skeletal remains from the colour of roots of teeth. Dent J 1977;43(2):836. [8] Solheim T. Dental color as an indicator of age. Gerodontics 1988;4(3):11418. [9] Gustafson G. Age determinations on teeth. J Am Dent Assoc 1950;41(1):4554. [10] Lamendin H, Baccino E, Humbert JF, Tavernier JC, Nossintchouk RM, Zerilli A. A simple technique for age estimation in adult corpses the 2 criteria dental method. J Forensic Sci 1992;37(5):13739. [11] Solheim T. Amount of secondary dentin as an indicator of age. Scand J Dent Res 1992;100(4):1939. [12] Kvaal SI, Kolltveit KM, Thomsen IO, Solheim T. Age estimation of adults from dental radiographs. Forensic Sci Int 1995;74(3):17585. [13] Paewinsky E, Pfeiffer H, Brinkmann B. Quantification of secondary dentine formation from orthopantomograms a contribution to forensic age estimation methods in adults. Int J Legal Med 2005;119(1):2730. [14] Meinl A, Tangl S, Pernicka E, Fenes C, Watzek G. On the applicability of secondary dentin formation to radiological age estimation in young adults. J Forensic Sci 2007;52 (2):43841. [15] Cameriere R, Ferrante L, Belcastro MG, Bonfiglioli B, Rastelli E, Cingolani M. Age estimation by pulp/tooth ratio in canines by peri-apical X-rays. J Forensic Sci 2007;52 (1):16670. [16] Cameriere R, De Luca S, Aleman I, Ferrante L, Cingolani M. Age estimation by pulp/tooth ratio in lower premolars by orthopantomography. Forensic Sci Int 2012;214(1-3):10512. [17] Solheim T. Dental root translucency as an indicator of age. Scand J Dent Res 1989;97 (3):18997. [18] Ramsthaler F, Kettner M, Verhoff MA. Validity and reliability of dental age estimation of teeth root translucency based on digital luminance determination. Int J Legal Med 2014;128(1):1716. [19] Gonzalez-Colmenares G, Botella-Lopez MC, Moreno-Rueda G, Fernandez-Cardenete JR. Age estimation by a dental method: a comparison of Lamendin’s and Prince & Ubelaker’s technique. J Forensic Sci 2007;52(5):115660. [20] Solheim T. Dental cementum apposition as an indicator of age. Scand J Dent Res 1990;98:51019.
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[21] Bocuto˘glu O, Yakan B. Coronal displacement of cementum: correlation between age and coronal movement of cementum in impacted teeth. Aust Dental J 1997;42(3):1858. [22] Kvaal SI, Solheim T. Incremental lines in human dental cementum in relation to age. Eur J Oral Sci 1995;103(4):22530. [23] Aggarwal P, Susmita S, Bansal P. Incremental lines in root cementum of human teeth: an approach to their role in age estimation using polarizing microscopy. Indian J Dent Res 2008;19:32630. [24] Maples WR, Rice PM. Some difficulties in the gustafson dental age estimations. J Forensic Sci 1979;24(1):16872. [25] Lucy D, Pollard AM. Further comments on the estimation of error associated with the gustafson dental age estimation method. J Forensic Sci 1995;40(2):2227. [26] Johanson G. Age determination from human teeth. Odont Revy 1971;22(21):40126. [27] Bang G, Ramm E. Determination of age in humans from root dentin transparency. Acta Odontol Scand 1970;28(1):335. [28] Maples WR. Improved technique using dental histology for estimation of adult age. J Forensic Sci 1978;23(4):76470. [29] Solheim T. A new method for dental age estimation in adults. Forensic Sci Int 1993;59 (2):13747. [30] Solheim T. Dental age estimation an alternative technique for tooth sectioning. Am J Forensic Med Path 1984;5(2):1814. [31] Mandojana JM, Martin-de las Heras S, Valenzuela A, Valenzuela M, Luna JD. Differences in morphological age-related dental changes depending on postmortem interval. J Forensic Sci 2001;46(4):88992. [32] Valenzuela A, Martin-de las Heras S, Mandojana JM, Luna JD, Valenzuela M, Villanueva E. Multiple regression models for age estimation by assessment of morphologic dental changes according to teeth source. Am J Forensic Med Path 2002;23(4):3869. [33] Martin-de las Heras S, Valenzuela A, Bellini R, Salas C, Rubino M, Garcia JA. Objective measurement of dental color for age estimation by spectroradiometry. Forensic Sci Int 2003;132(1):5762. [34] Devos N, Willems G, Wood R. Objective human tooth colour measurements as a means of determining chronologic age in vivo and ex vivo. J Forensic Odontostomatol 2009;27:28. [35] (ASTM) ASoTaM. Standard test method for indexes of whiteness and yellowness of nearwhite, opaque materials. West Conshohocken, PA: ASTM; 1993. [36] (CIE) ICoI. Colorimetry. 2nd ed. Vienna: Central Bureau of the CIE; 1986. [37] Martin-de-las-Heras S, Mar Del R, Molina A, Rubio L. Spectrophotometric dental colour measurement to assess age in living adults. Aust J Forensic Sci 2016;50:829. [38] Vandevoort FM, Bergmans L, Van Cleynenbreugel J, Bielen DJ, Lambrechts P, Wevers M, et al. Age calculation using X-ray microfocus computed tomographical scanning of teeth: a pilot study. J Forensic Sci 2004;49(4):78790. [39] Ge ZP, Yang P, Li G, Zhang JZ, Ma XC. Age estimation based on pulp cavity/chamber volume of 13 types of tooth from cone beam computed tomography images. Int J Legal Med 2016;130(4):115967. [40] Star H, Thevissen P, Jacobs R, Fieuws S, Solheim T, Willems G. Human dental age estimation by calculation of pulp-tooth volume ratios yielded on clinically acquired cone beam computed tomography images of monoradicular teeth. J Forensic Sci 2011;56: S7782.
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[41] Martin-de las Heras S, Valenzuela A, Villanueva E. Deoxypyridinoline crosslinks in human dentin and estimation of age. Int J Legal Med 1999;112(4):2226. [42] Martin-de las Heras S, Valenzuela A, Overall CM. Gelatinase A in human dentin as a new biochemical marker for age estimation. J Forensic Sci 2000;45(4):80711. [43] Takasaki T, Tsuji A, Ikeda N, Ohishi M. Age estimation in dental pulp DNA based on human telomere shortening. Int J Legal Med 2003;117(4):2324. [44] Ohtani S, Yamamoto K. Age estimation using the racemization of amino-acid in human dentin. J Forensic Sci 1991;36(3):792800. [45] Ohtani S, Yamamoto T. Age estimation by amino acid racemization in human teeth. J Forensic Sci 2010;55(6):16303.
Dental age estimation in adults Stella Martin de las Heras Forensic Medicine and Forensic Dentistry Department, School of Medicine, Granada, Spain
9.1 Introduction Age estimation in adults is a complex issue for forensic scientists because the aging process is influenced by numerous factors, including lifestyle, nutrition, type of work, exercise, toxic habits, diseases, and treatments. Age estimation in the unidentified dead can contribute to a biological profile for comparison with data on missing persons, while age estimation in living adults without valid identification documents plays an important role in criminal and civil proceedings [1]. The demand for age estimates of living persons has markedly increased over recent years due to a major rise in the migration of populations [2]. Dental age estimation techniques are considered to be highly reliable in children but less accurate in adults; however, because the age interval for adults is much wider, age estimation errors of 6 710 years can be acceptable in adults but not in children. Nevertheless, age assessment is more difficult in adults, and there is no consensus on the most appropriate technique. When all permanent teeth have been formed, age can no longer be estimated by studying their development, although some changes in dental tissues and related structures are produced by the normal aging process. Biochemical and morphological methods have been used to evaluate degenerative processes undergone by teeth over time. Morphological methods are the most widely used in practical forensic cases and include the study of secondary dentin formation, root translucency, cement apposition, and dentin color, among others. Biochemical studies are more complex and involve destruction of the teeth [3]. The choice of dental age estimation techniques depends on the circumstances. Thus, destructive techniques, tissue sampling, and radiological exposure are always acceptable in dead but not living adults, while even low-dose exposure is considered inappropriate for age determination in some countries and other ethically viable approaches must be applied [4,5].
Age Estimation. DOI: https://doi.org/10.1016/B978-0-12-814491-6.00009-1 Copyright © 2019 Elsevier Inc. All rights reserved.
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9.2 Morphological dental age-related changes The most frequently studied age-related morphological changes in teeth are dental attrition or wear, tooth color, periodontal recession, secondary dentin deposition, dentin transparency, root resorption, root surface roughness, and cementum apposition. G
G
G
G
Attrition. This refers to the reduction in dental tissue by contact between antagonist teeth, causing wear facets on occlusal aspects or incisal margins. Given that attrition can also be caused by factors other than age, including diet, habits/customs, diseases (e.g., bruxism), and type of occlusion, among others, it is accepted for age estimation only in combination with other parameters [6]. Tooth color. Age-related changes in enamel color are caused by increased nitrogen content and surface cracking, which leads to light refraction changes. Changes in dentin color are attributable to modifications in mineral and organic composition. The root is less frequently affected by pathological processes in comparison to the crown (e.g., caries or staining), which is more exposed to external factors that alter tooth color. Hence, color should be measured whenever possible in the proximal third of root dentin after elimination of the cementum, which involves tooth extraction [7,8]. Color can be measured on the enamel in living individuals, but the greater exposure of this tissue to external factors should be taken into account. Periodontal recession. The original term used by Gustafson in his original [9] and subsequent studies was Periodontosis, but this designation is now considered obsolete. Older age is accompanied by the destruction of periodontal fibers at the cervical margin, progressing toward the apex, and periodontal recession is defined as the maximum distance between the cementoenamel junction and the soft-tissue attachment line (Fig. 9.1) and is measured on the vestibular aspect [10]. However, recession not directly related to age can be observed in acute or chronic inflammatory processes such as periodontal disease and can even result from periodontal treatment. A further limitation of its use to estimate age is that the soft-tissue attachment line is often not visible in skeletonized remains. Secondary dentin. Secondary dentin is produced after complete tooth formation and reduces the pulp cavity size. It commences in the coronal region of the pulp, where contact is made with the antagonist tooth during mastication. When coronal pulp filling is complete, secondary dentin formation continues through the root canal or canals. This change can be measured by direct observation of the sectioned tooth under binocular loupes [11] or by indirect X-ray observation to calculate the pulp size [1216]. Both secondary dentin quantification and pulp cavity reduction can be used as age estimation methods in forensic studies.
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FIGURE 9.1 Image of tooth showing the attachment line of periodontal soft tissues. G
G
Root transparency. This phenomenon is caused by an increase in peritubular dentin deposits, which changes the refractive index of root dentin. It commences in the dentin closest to the apex and extends toward the crown over the years (Fig. 9.2). Root transparency is considered one of the factors most closely associated with age and has been recommended as a single indicator for age calculation [10,1719]. Among the different root translucency measurements considered (length, area, width), the most widely used is the maximum length of the translucent area from apex to crown, usually on the distal aspect of the tooth. Root resorption and increased root surface roughness. The root surface of teeth becomes rougher and more irregular over the years. Root roughness measurement under binocular loupe is combined with assessment of the defects produced by root resorption, which can also be affected by orthodontic treatments, periapical disease, and dental trauma. However, root resorption alone has shown a low correlation with age and should only be considered in combination with other dental changes for age estimation purposes [20].
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FIGURE 9.2 Detailed image of the apical area of sectioned tooth, showing dentin transparency.
G
Cementum apposition. Apposition of cementum on the tooth root is observed with older age. Acellular cementum is observed in the coronal half of the root but a mixture of cellular and acellular cementum predominates in the apical half. Cementum apposition varies widely among different root localizations, being maximum in the apex and minimum close to the cementoenamel junction. It has been reported to augment with older age, with findings of a threefold increase in the cementum layer between the ages of 11 and 70 years [21]. In most mammals, including humans, cementum apposition takes place in phases, resulting in two types of layer with distinct color and optical properties. Narrow dark-stained lines (incremental lines) are separated by wide cementum bands and the count of incremental lines has been used as an age estimation method, although it has serious limitations [22,23].
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9.3 Morphological dental age estimation methods The morphological changes undergone by teeth over time have been used to develop numerous predictive models for age estimation. We have selected here the most important and practically useful methods. The first scientific method based on tooth sections was published in 1950 by Gustafson [9], who macroscopically assessed six age-related parameters: attrition (A), secondary dentin formation (S), periodontal recession (P), cementum apposition (C), root resorption (R), and root transparency (T), with each parameter scored on a 4-point scale (03 points). Using the sum of the scores, he obtained a regression line for age estimation. Applying the formula Age 5 11.43 1 4.56x, he estimated age with an approximate error of 6 78 years, although this result has not been reproduced in subsequent studies [24,25]. In general, it is now accepted that the mean error of Gustafson’s method is 6 1015 years. Gustafson’s method [9] had a major impact in the field of dental age estimation in adults and formed the basis for the development of new techniques that take account of additional parameters (e.g., tooth type, sex, and/or ancestry). In 1971, Johanson [26] studied the same six age-related structural changes as Gustafson [9] but applied a 7-point (grade) scale (Fig. 9.3). He used multiple regression analysis to establish a general formula in which different contributions are made by the distinct dental parameters as follows: Age 5 11.02 1 5.14A 1 2.3S 1 4.14P 1 3.71C 1 5.57R 1 8.98T. The error of this method was 6 5.2 years, considering a standard deviation of 6 10 years with two standard deviations for the analysis of multiple teeth or of 6 16 years with two standard deviations for the analysis of a single tooth. Johanson took no account of the type of tooth or the sex of the individual in his method.
FIGURE 9.3 Drawing showing the seven different grades used for the six parameters analyzed in the Johanson’s method. Johanson G. Age determination from human teeth. Odont Revy 1971;22(21):40126.
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Lamendin [10] developed an age estimation method considering root height, periodontal recession, and root transparency. Age 5 (0.18 3 P) 1 (0.42 3 T) 1 25.53, where P is defined as P 5 (measured periodontal recession height 3 100)/measured root height and T 5 (measured root transparence height 3 100)/measured root height. This method should not be applied to individuals below 40 years due to inaccuracy shown in younger individuals. Bang and Ramm [27] developed an age estimation method that considered root dentin transparency alone, being the parameter least influenced by external factors, sex, or ancestry. Root transparency length (mm) is measured from the apex in intact or sectioned teeth, which should be cut in a vestibularlingual direction, preserving the dental structure in the center of the pulp and showing the apex. If the area of translucency is not straight, the mean of the maximum and minimum measurements is taken. In the Bang and Ramm method, age is estimated from one formula if the root translucency length is # 9 mm and from another if it is .9 mm, because of a slowing in dentin production after the age of 70 years. Both formulas also take into consideration the type of tooth (Table 9.1). However, one shortcoming of this method is that the length of translucent dentin is expressed in absolute values and is not related to the total length of the root. In 1978, Maples [28] developed age estimation formulas for different types of teeth that considered secondary dentin (S) and root translucency (T) alone, using the 7-point scale of Johanson. They achieved highly acceptable results, with estimation errors ranging between 6 6.8 and 6 12.2 years according to the type of tooth under study (Table 9.2). Solheim [29] proposed a formula for each type of single-rooted tooth (upper and lower), selecting parameters that showed the strongest correlation with age. He also developed another multiple regression model for age estimation in which tooth color and sex were included as independent variables. Thus, for the first upper premolar, age estimation is based on the formula: Age 5 8 1 7.3CEST 1 4.1SJ 1 1.4TID; where CEST is the color of the tooth estimated with a color guide; SJ, secondary dentin formation according to the Johanson method; and TID, the length of dentin transparency (mm) in the intact tooth. The standard error of this method ranges between 6 7.0 and 6 12.9 years according to the tooth type. In the case of the formula for the first upper premolar cited above, the error is 6 10.8 years (Table 9.3). Application of Solheim’s method [29] usually requires the utilization of handpiece and drill to obtain a mesial to distal vestibulolingual section up to the middle pulp area (Fig. 9.4). This “half-tooth” technique, first described in 1984 [30], avoids the loss of apex with curvature that limited previously used thin-slice techniques. After preparation of the tooth, the different measurements are made under a stereomicroscope (magnifying glass) with the assistance of a planigraph.
TABLE 9.1 Data for age determination and standard deviation (SD) in years according to the Bang and Ramm’s method [27] # 9 mm Intact roots
.9 m Sectioned roots
Intact roots
Sectioned roots
Type of tooth
B0
B1
B2
SD
B0
B1
B2
SD
B0
B
SD
B0
B
SD
Upper right central incisor
20.3
5.74
0.000
10.42
21.02
6.03
2 0.060
11.43
20.34
5.74
10.42
22.36
5.39
11.25
Upper left central incisor
24.3
6.22
2 0.119
9.71
26.84
6.00
2 0.155
11.03
26.78
4.96
9.58
30.18
4.30
10.93
Upper right lateral incisor
18.8
7.10
2 0.164
10.83
23.09
7.04
2 0.197
11.25
22.06
5.36
10.73
25.55
5.23
11.20
Upper left lateral incisor
20.9
6.85
2 0.223
9.77
24.62
5.18
2 0.077
9.40
25.57
4.38
9.81
25.90
4.39
9.24
Upper right cuspid
26.2
4.64
2 0.044
12.59
21.52
6.49
2 0.171
13.29
28.13
4.01
12.39
28.01
4.23
13.12
Upper left cuspid
25.27
4.58
2 0.073
13.80
24.64
5.22
2 0.143
13.78
27.59
3.65
13.60
29.41
3.32
13.70
Upper first premolars
23.91
3.02
0.203
11.62
29.98
2.73
0.107
13.17
18.42
5.40
11.41
28.44
3.81
12.93
Upper right second premolar
23.78
5.06
2 0.064
11.21
24.76
4.81
0.000
10.43
25.33
4.28
10.96
24.75
4.81
10.43
Upper left second premolar
25.95
4.07
2 0.067
9.48
22.34
7.59
2 0.393
6.88
26.92
3.37
9.32
26.21
4.03
7.62
(Continued )
TABLE 9.1 (Continued) # 9 mm Intact roots
.9 m Sectioned roots
Intact roots
Sectioned roots
Lower right central incisor
9.80
12.61
2 0.711
10.91
13.63
12.11
2 0.683
11.01
29.00
4.23
11.85
31.78
4.19
12.09
Lower left central incisor
23.16
9.32
2 0.539
12.27
26.46
8.79
2 0.511
11.74
37.56
2.94
12.84
37.89
3.08
12.31
Lower right lateral incisor
26.57
7.81
2 0.383
11.95
21.77
10.19
2 0.581
11.26
38.81
2.81
12.43
38.49
3.03
12.42
Lower left lateral incisor
18.58
10.25
2 0.538
10.08
22.22
9.07
2 0.444
10.78
33.65
3.53
11.12
35.19
3.49
11.46
Lower right cuspid
23.30
8.45
2 0.348
10.52
24.34
8.38
2 0.358
10.74
37.80
3.50
11.24
40.32
3.05
11.77
Lower left cuspid
27.45
7.38
2 0.289
10.22
23.88
8.76
2 0.388
9.96
41.50
2.84
10.74
42.07
2.73
10.88
Lower right first premolar
24.83
6.85
2 0.237
9.53
21.54
8.63
2 0.395
10.19
30.83
4.05
9.73
33.10
3.66
11.08
Lower left first premolar
29.17
5.96
2 0.173
10.13
26.02
7.00
2 0.234
8.39
34.97
3.74
10.14
32.79
4.11
8.71
Lower right second premolar
29.42
4.49
2 0.065
13.00
14.90
9.93
2 0.451
9.05
30.68
3.76
12.82
27.46
4.17
11.23
Lower left second premolar
18.72
5.79
2 0.082
11.52
23.87
5.50
2 0.098
10.85
20.87
4.79
11.38
25.60
4.41
10.77
Upper 6 molars, mesial root
30.25
3.23
2 0.018
10.22
28.22
4.82
2 0.101
6.85
30.56
3.00
10.08
30.03
3.48
7.11
Lower 6 molars, mesial root
27.39
6.25
2 0.239
10.49
33.42
5.18
2 0.302
15.76
30.32
3.66
11.04
35.27
2.78
15.74
Upper 6 molars, distal root
34.73
0.67
0.211
10.68
20.43
6.09
2 0.182
10.85
29.49
3.32
11.09
26.89
3.55
10.92
Lower 6 molars, distal root
30.21
5.52
2 0.181
10.69
29.91
4.97
2 0.102
11.10
31.46
3.77
10.89
30.31
4.22
11.00
Upper 6 molars, palatinal root
27.43
3.64
0.039
10.02
25.15
4.34
2 0.032
10.20
26.81
4.07
9.98
25.83
3.95
10.16
When the translucent dentin length (X) is # 9 mm, the formula applied is AGE 5 B0 1 B1X 1 B2X2. When the translucent dentin length (X) is .9 mm, the formula is AGE 5 B0 1 BX. Source: Bang G, Ramm E. Determination of age in humans from root dentin transparency. Acta Odontol Scand 1970;28(1):335.
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TABLE 9.2 Formulas for age calculation in different types of teeth and the standard error (SE) in years according to the Maples method [28] Formula
SE (years)
Central incisor
3.89S 1 14.23T 1 15.28
9.1
Lateral incisor
6.51S 1 12.55T 1 25.26
9.6
Cuspid
18.67S 1 11.72T 1 21.94
11.0
First bicuspid
2.82S 1 15.25T 1 19.65
12.2
Second bicuspid
4.79S 1 15.53T 1 17.99
7.6
First molar
11.28S 1 5.32T 1 10.86
11.1
Second molar
6.99S 1 10.86T 1 19.31
6.8
Third molar
4.71S 1 12.30S 1 24.57
12.0
S, Secondary dentin as Johanson’s method; T, root transparency as Johanson’s method. Source: Maples WR. Improved technique using dental histology for estimation of adult age. J Forensic Sci 1978;23(4):76470.
Over the past few years, methods based on the above parameters have been developed using technologies that avoid examiner subjectivity, such as automatized image analysis. In this line, Mandojana et al. [31] proposed formulas to determine age in worn teeth (according to the half-tooth technique). They measured the dental parameters in the usual manner and also automatically (e.g., proportional pulp cavity size and root dentin transparency), digitalizing the half-tooth image and applying the Visilog 5 morphometric analysis program (Noesis S.A., Quebec, Canada). The standard error for age estimation by this method was 6 9.9 years [31,32]. More recently, authors [18] used image analysis techniques for the quantification of dentin transparency alone, acquiring a digital image of the extracted (intact) tooth and using the Photoshop CS4 program (Adobe systems Inc., San Jose, CA, USA) to calculate the mean luminance value (cd/m2) of the translucent dentin area. They obtained an age estimation error ranging between 6 6.26 and 6 7.69 years, although the authors stipulated that new calibrations are required if a different camera is used. Tooth color has been found to make a significant contribution to age estimation based on dental morphological changes and is included in 7 out of the 10 formulas proposed by Solheim for this purpose [29]. Tooth color estimation by visual comparison with a known standard (tooth shade guide) has been widely utilized by forensic odontologists, but the capacity to distinguish colors varies among observers [7,8,29]. Nevertheless, despite the shortcomings of this subjective approach, little attention appears to have been paid to the development of improved dental color measurement
TABLE 9.3 Formulas for age estimation in different types of tooth and the error (standard error of estimates) in years, according to Solheim’s method [29] Formula
R2
SES
1
AGE 5 24.3 1 8.7CEST 1 5.2TD 2 2.3CAP 2 4.3SEX
0.83
7.0
2
AGE 5 38.7 2 126ST 1 4.7CEST 1 4.2TD 1 0.05C1
0.82
8.0
3
AGE 5 10.1 1 2.3TID 1 4.4SJ 1 6.1CEST
0.77
8.4
4
AGE 5 8.0 1 7.3CEST 1 4.1SJ 1 1.4TID
0.69
10.8
5
AGE 5 6.1 1 9.1CEST 1 3.3AJ 1 7.3LPMEAN 1 1.4TID
0.79
7.9
1
AGE 5 221.8 2 55.3SC 1 32.8LC1 2 10.3SEX 1 2.6TID
0.76
9.5
Tooth Maxilar
Mandibular
2
AGE 5 224.5 1 4.9CEST 1 2.1TID 2 7.0SEX 1 20.1LC1 1 2.4AJ
0.73
9.2
3
AGE 5 19.2 1 1.7TID 1 5.1CEST 1 3.5SJ
0.66
10.8
4
AGE 5 228.1 1 3.0TID 1 0.6ARA 1 24.1LC1 2 5.6SEX 1 7.3LPMEAN
0.77
8.7
5
AGE 5 7.5 1 2.7TID 1 4.9SJ 1 4.9SRS
0.58
12.9
Teeth: 1: central incisor; 2: lateral incisor; 3: canine; 4: first premolar; 5: second premolar. SES, Standard error of estimates; CEST, color estimate score; TD, translucency scored according to Dalitz; CAP, crown pulp area in square mm; ST, sum of pulp diameters/sum of root diameters; C1, sum of cementum thichness on vestibular and lingual surfaces; TID, length in mm of translucent zone in dry intact tooth; SJ, secondary dentin scored according to Johanson; AJ, attrition scored according to Johanson; LPMEAN, logarithmic transformation of periodontal recession; SC, pulp diameter/root diameter at the cervical area; LC1, logarithmic transformation of C1 (base 10); ARA, area of attrition in square mm; SRS, surface roughness score. Source: Solheim T. A new method for dental age estimation in adults. Forensic Sci Int 1993;59(2):13747.
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FIGURE 9.4 Photograph of a worn tooth in accordance with the half-tooth technique of Solheim [30].
techniques for age estimation [33,34]. A spectrophotometer measures the color of an observed object by reflection or transmission and gives the entire spectral curve, being limited to the visible frequency range (usually 350800 nm). Spectrophotometry quantifies color by chromaticity coordinates (x, y, z) or colorimetric variables (L , a , b ) and permits the calculation of whiteness (WI) or yellowness indexes (YI) [35,36]. The advantage of spectrophotometry is that it provides sufficient information to calculate color values for any illuminant and automatically detects metamerism, that is, color changes under different lighting and at different angles. Age-related color changes can be measured in both dentin and enamel. Dentin is more isolated from external factors and is therefore a better
Dental age estimation in adults Chapter | 9
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candidate to determine age but this is an invasive method that can only be applied to cadavers. Dentin color has been used for age estimation alone and in combination with other age-related factors [8,29,33]. Enamel color may be useful in living individuals, especially in countries that consider even low-dose exposure to radiological imaging to be inappropriate for age determination. Spectrophotometry studies have demonstrated that enamel becomes less white and more yellow over the years [37], and receiver operating characteristic (ROC) analysis can be used to estimate the likelihood of an individual being over a certain age based on enamel color (Table 9.4). An area under the ROC curve (AUC) above 0.7 was achieved for enamel color measurements by spectrophotometry, indicating that it may be a useful method for forensic purposes, despite the potential effects on enamel of numerous external factors. [37]. For instance, when the WI of an upper incisor is ,5.92, an age .30 years can be estimated (AUC 5 0.8) with 75% sensitivity and 92% specificity, and when its YI is .28.59, an age .75 years can be estimated (AUC 5 0.7) with 91% sensitivity and 70% specificity. Age estimation radiographic methods that measure pulp cavity narrowing due to secondary dentin formation are noninvasive and can therefore be applied in living individuals. Cameriere et al. [15,16] used digitalized panoramic X-ray to calculate pulp and tooth area proportions using Photoshop CS4 software (Adobe systems Inc., San Jose, CA, USA). In this technique, the operator selects the total tooth area, marking a minimum of 20 points on the contour with a polygonal lasso tool. The pulp area is then selected, using a minimum of 10 points, and the program automatically calculates the value in pixels of each area. The author proposed age estimation formulas for different tooth types alone and in combination. For instance, the regression equation for the combination of teeth 34 and 35 (FDI notation) is as follows: Age ðyearsÞ 5 93:55 ð360:43 3 R34 Þ 2 ð380:69 3 R35 Þ 1 ð1855:3 3 R34 3 R35 Þ where R is the ratio of pulp to tooth area in pixels. The error (standard deviation) for this formula was 6 6.29 years. There is current research interest in the use of cone beam computed tomography images for the volumetric measurement of pulp chamber narrowing as an age estimation method [3840]. Three-dimensional techniques are expected to provide a more precise calculation in comparison to two-dimensional methods, but further research is warranted in larger samples to demonstrate their usefulness.
9.3.1
Age-related biochemical changes for dental age estimation
Tooth dentin consists of approximately 91% collagen and is an excellent substrate, being protected from many extrinsic environmental factors and being stable, except for small amounts of secondary and tertiary dentin
TABLE 9.4 Receiver operating characteristic (ROC) analysis for age estimation with colorimetric variables (L , a , b ), chromaticity coordinates (x, y, z), or whiteness (WIC, Z%, WI) and yellowness (YI) indexes measured by spectrophotometry in the enamel of upper incisors $ 30 years AUC
a
Youden
b
$ 40 years c
Se
Sp
d
AUC
a
Youden
b
Se
$ 50 years c
Sp
d
AUC
a
Youden
b
Se
$ 65 years c
Sp
d
AUC
a
Youden
b
Se
$ 70 years c
Sp
d
AUC
a
Youden
b
Se
$ 75 years c
Sp
d
AUC
a
Youdenb Sec Spd
L
0.6
60.74
77
57
0.5
60.74
78
42
0.5
49.81
95
18
0.5
50.92
94
19
0.5
59.23
73
41
0.5
52.04
91
20
a
0.8
0.36
87
71
0.7
0.92
66
67
0.7
0.27
90
38
0.7
0.94
78
50
0.7
0.78
93
46
0.7
2.69
64
74
b
0.8
9.21
59
85
0.7
9.21
66
78
0.8
9.22
75
72
0.7
10.09
78
70
0.8
10.43
86
76
0.8
10.44
91
74
x
0.8
0.335
78
85
0.7
0.336
80
64
0.7
0.335
88
52
0.7
0.342
78
66
0.8
0.340
93
53
0.8
0.344
91
51
y
0.7
0.351
56
92
0.7
0.351
65
85
0.7
0.353
70
79
0.7
0.353
78
59
0.7
0.354
80
71
0.7
0.354
82
69
z
0.8
0.309
71
92
0.7
0.304
70
75
0.8
0.301
72
72
0.7
0.300
78
60
0.8
0.301
93
58
0.8
0.298
82
65
WIC 0.8
2 34.28
74
92
0.8
2 34.28
78
67
0.8
2 37.33
81
63
0.7
2 42.35
72
60
0.8
2 38.02
93
54
0.8
2 42.35
91
60
Z%
0.7
25.36
87
57
0.6
25.36
91
42
0.6
25.36
93
31
0.6
23.15
79
30
0.6
19.68
73
54
0.6
22.27
81
36
WI
0.8
5.92
75
92
0.8
4.41
71
75
0.8
4.40
79
68
0.7
2.76
78
67
0.8
2.73
86
67
0.8
2.76
91
65
YI
0.8
25.77
68
92
0.8
26.47
68
78
0.8
25.22
86
61
0.7
28.25
78
70
0.8
28.54
86
72
0.8
28.59
91
70
a
AUC: Area under the ROC curve. Youden: Cutoff point that maximizes the sum of sensitivity and specificity. Se: Sensitivity. d Sp: Specificity. Source: Martin-de-las-Heras S D-RM, Molina A, Rubio L. Spectrophotometric dental colour measurement to assess age in living adults. Aust J Forensic Sci 2016;50:829. b c
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formation. Age-related biochemical changes in dentin include an increase in double collagen bonds, specifically deoxypyridinoline [41]; a decrease in gelatinases, enzymes responsible for degrading type I collagen [42]; a shortening of dental pulp DNA telomeres [43]; and the aspartic acid racemization [44,45]. This latter research line has strengthened over the past few decades, demonstrating that aging is accompanied by a change in amino acid spatial conformation (racemization), with an increase in dextrorotatory (D) over levorotatory (L) forms. All amino acids that form the proteins of the organism belong to stereochemical series L, with optical activity, given the presence of at least one asymmetrical carbon atom in their molecule. D forms progressively increase over the years until an equimolar mixture of D and L enantiomers is reached, constituting an inactive optical form. Among all amino acids, aspartic acid has the highest racemization reaction rate, which occurs as a first-order chemical reaction, indicating a constant rate with higher age. D-aspartic acid readily accumulates in tissues that metabolize slowly, such as the intervertebral disc, lens, and teeth. Racemization can be affected by three environmental factors: body pH, humidity, and temperature. Body temperature can produce the greatest modification in racemization rates, which increase at higher temperatures. Nevertheless, aspartic acid racemization in dentin is the most frequently used biochemical technique for age estimation and yields precise results in adults, with an error of approximately 6 3 years [44,45]. However, biochemical studies are methodologically complex (requiring special laboratory facilities and experience), time-consuming, and costly, and they involve destruction of the teeth [3]. For these reasons, morphological methods continue being the most widely employed in practical forensic cases.
9.4 Conclusions Age estimation in adults is required for the identification of skeletonized remains, as in major catastrophes, and of living humans, especially in the context of the increasing migration of populations. This task continues to pose a challenge to forensic scientists because aging is a particularly individual process. This chapter has addressed different research approaches, focusing on techniques that represent an important advance in this field and are of practical usefulness. Selection of the most appropriate method depends upon the characteristics of each case, and practitioners need to be aware of the range of options available, including their validity, reliability, and measurement error. Dental age determination by invasive techniques, tissue sampling, and/or X-ray examination is highly controversial in living individuals. Recent technological advances have allowed age-related dental changes to be measured in a more objective manner. Forensic odontologists play a key role in the age determination and identification of children and adults, and there is a need for continuing research to increase the reliability and value of their evidence.
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