Reliability of Cephalogram in Determining Skull Gender Dimorphism

AUTHORS

Sidra Butt 1 , * , Imtiaz Ahmed 1

1 Department of Orthodontics, Dr. Ishrat ul Ebad Khan Institute of Oral Health Sciences, DUHS Karachi, Pakistan

How to Cite: Butt S, Ahmed I. Reliability of Cephalogram in Determining Skull Gender Dimorphism, Iran J Ortho. 2016 ; 11(1):e5321. doi: 10.17795/ijo-5321.

ARTICLE INFORMATION

Iranian Journal of Orthodontics: 11 (1); e5321
Published Online: May 30, 2016
Article Type: Research Article
Received: January 8, 2016
Accepted: January 30, 2016
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Abstract

Background: The forensic anthropologists have been vastly studied the dimorphism in teeth, hair, pelvis, skull and in bone sizes.

Objectives: To investigate the gender dimorphic potential of cephalometric parameters.

Materials and Methods: Thirteen angular and twenty-one linear lateral cephalometric measurements were analyzed on randomly selected manual tracings of sixty-nine male and sixty-nine female cephalograms between the ages twenty to fifty years.

Results: 91.3% males correctly classified in the data, on the basis of discriminant function we made, similarly, 97.1% females were correctly classified in their specific group by the help of this discriminant function. The percentage of skulls correctly classified with this function was 94.2%.

Conclusions: 94.2% of original grouped cases correctly classified. For higher results extensive research with large sample size and both linear and angular cranial dimorphic traits for gender identification is proposed.

Keywords

Skull Dimorphism Dental Radiology and Imaging Lateral Cephalogram Discriminant Function Forensic Dentistry

Copyright © 2016, Iranian Journal of Orthodontics. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

1. Background

Is the patient a male or a female? This is usually the first naturally occurring thought that comes into the clinician’s mind before going into the case detail. Determining sex is vital for an individuals’ identification from birth till death as it is a foundational component of the biological profile. Not only the diseases but even the growth, development and aging have gender specific features. The forensic anthropologists have been vastly studied the dimorphism in teeth, hair, pelvis, skull and in bone sizes. It is only possible once the male or female has reached adolescence or adulthood.

Second best region to determine dimorphism between the sexes is the human skull. Skull bones that are available as the human remains can be used for identification of sex for civil or criminal intend when there is limitation for the application of finger printing method of human identification. Dimorphic traits of human skull are used widely reporting 77 to 92% accuracy of anthropometric measurements. Whereas, the studies conducted for the sex discrimination using cephalograms have claimed 80 to 100% accuracy (1-3). It also has been calculated that a group of traits are required for precise diagnosis instead of deliberating single character. Sex-related skeletal features are not obvious in children’s bones. Elusive differences are detectable but they become more defined following puberty and sexual maturation.

Zeh et al. (2) stated determination of sex with 95% reliability when using pelvis alone, 92% using the skull alone and 98% using both the pelvis and the skull.

Other than its orthodontic use, the readily available equipment for cephalogram can make it a practicable tool of the forensic investigation as well specifically in medico-legal cases of unidentified severed heads in events of burn, murder, accidents, suicide bombing and war.

Hence, by using lateral cephalograms, this descriptive study is undertaken to evaluate potential sex differences in linear and angular lateral cephalometric readings. Discriminant function analysis is applied statistical technique for sex discrimination. The aim of the analysis is to determine whether these variables will discriminate between the gender or not.

2. Objectives

To investigate the gender dimorphic potential of cephalometric parameters.

3. Materials and Methods

The sample size for this study consists of 138 subjects. Study population comprised of adults, 69 males and 69 females between the ages 18 years to 50 years, visited Department of Orthodontics, Dr. Ishrat ul Ebad Khan institute of oral health sciences, Karachi, Pakistan. Good quality lateral cephalograms of all subjects of known sexes are randomly selected. Cephalometric points were located and marked by a single investigator. Individuals with the history of facial asymmetry, trauma, hereditary, congenital, developmental or nutritional disturbances, prolonged illness, previous orthodontics or orthognathic treatment or surgery of skull are not included.

The undermentioned linear and angular parameters (Table 1: Linear (mm) Cephalometric Variables and Table 2 Angular (°) Cephalometric Variable) were measured by using lateral cephalometric landmarks (Figure 1: landmarks) traced on acetate paper (Figure 2 Cephalometric parameters: Linear and Figure 3 Cephalometric parameters: Angular).

Table 1. Linear (mm) Cephalometric Variables
VariablesDescription
Ba-ANSMost inferior posterior point in the sagittal plane on the anterior rim of foramen magnum(Ba) to the tip of the bony anterior nasal spine in the median plane(ANS)
Ba-NMost inferior posterior point in the sagittal plane on the anterior rim of foramen magnum(Ba) to the most anterior point of the frontonasal suture in the median plane(N)
N-ANSThe most anterior point of the frontonasal suture in the median plane(N) to the tip of the bony anterior nasal spine in the median plane(ANS)
N-MeThe most anterior point of the frontonasal suture in the median plane(N) to the most inferior midpoint on the mandibular symphysis(Me)
Fs-WdFrontal sinus width
Fs-HtDistance between the upper limit( V1 ) and lower limit(V2) of frontal sinus
Ma-SNLowest point of mastoid bone(Ma) to sella-nasion plane(SN)
Ma-FHLowest point of mastoid bone(Ma) to Frankfort plane(FH)
Ma-HtPorion to mastoidale
Ma-wdMaximum width of the mastoid in the anterior-posterior direction.
ANS-MeLower anterior facial height
GSgNDistance between glabella and the supraglabellare to nasion line
SgGMDistance between supraglabellare and the glabella to metopion line
S’-Co’Projection of sella on FH plane-projection of Condylion on FH
Sella lengthThe distance from tuberculum sella to posterior clinoid
Sella widthThe largest antero-posterior dimension
Sella height anteriorThe vertical distance, as measured perpendicular to the FH plane, from tuberculum to the sella floor
Sella height posteriorThe vertical distance, as measured perpendicular to the FH plane, from posterior clinoid to the sella floor
ULTcRatio of total chin thickness to upper lip thickness
GPIGlabella projection index = GSgN × 100/SgN
G-SI-PIGlabella superior-inferior projection index-a measure of the location of glabella along the midsagittal plane = GSg/GN.
Table 2. Angular (°) Cephalometric Variable
VariableDescription
GSgMAngle between the metopion to supraglabellare line and the supraglabellare to glabella line
GMBaNAngle between the glabella to metopion line and the basion to nasion line
GMSNAngle between the glabella to metopion line and the sella to nasion line
GMFHAngle between the glabella to metopion line and the porion to orbitale line
Ba-N-Me Angle between basion and nasion and menton
Me-N-ANSAngle between menton and nasion and anterior nasal spine
S-N-MeAngle between sella and nasion and menton
SNArAngle between SN plane and articulare
GNGSgAngle between lines glabella-nasion and glabella-supraglabella
GSGMAngle between lines glabella-sella and glabella-metopion
GS-SNAngle between lines glabella-sella and sella-nasion
SN-SMaAngle between lines sella-nasion and sella-mastoidale
GS-GNAngle between lines glabella-sella and glabella-nasion
Figure 2. Cephalometric Parameters: Linear
Cephalometric Parameters: Linear

I, Ba-ANS; II, Ba – N; III, N – ANS; IV, N – Me; V, Fs – Wd; VI, Fs – Ht; VII, Ma – SN; VIII, Ma – FH; IX, Ma – Ht; X, Ma – wd; XI, ANS – Me; XII, GSgN; XIII, SgGM; XIV, S’ – Co’; XV, Sella length; XVI, Sella width; XVII, Sella height anterior; XVIII, Sella height posterior; XIX, ULTc; XX, GPI; XXI, G-SI-PI.

Figure 3. Cephalometric Parameters: Angular
Cephalometric Parameters: Angular

I, GSgM; II, GMBaN; III, GMSN; IV, GMFH; V, Ba-N-Me; VI, Me-N-ANS; VII, S-N-Me; VIII, SNAr; IX, GNGSg; X, GSGM; XI, GS-SN; XII, SN-SMa; XIII, GS-GN.

3.1. Statistical Analysis and Results

The data was analyzed employing statistical software SPSS version 16.0. Direct discriminant function analysis applied to calculate specific discriminant function equation for all parameters. It selects the minimum number of traits yielding maximum discriminatory effectiveness. Statistically the mean differences of all the measurements were significant. Mean female values of all linear variables were smaller than the male mean values except ULTc which is larger in females by 0.3 mm while all the angular measurements are smaller in males except GMSN, GMFH, GMBaN, GNGSg and GSGM with the P value 0.000. The result of all the variables are presented in Table 3 (Group Statistics according to sex and in total).Group means and standard deviations with large separations indicating these variables may be good discriminants. Wilks’ lambda indicates the significance of the discriminant function. Table 4 (Wilk’s Lambda) indicates a highly significant function (P < .000) and provides the proportion of total variability not explained, so we have 27.8% unexplained. The resulted canonical discriminant 0.850 showed high correlation between the discriminate function and independent variables. Table 5 Functions at Group Centroids (Unstandardized canonical discriminant functions evaluated at group means) shows male are more associated in classification as compare to females. Females have negative association in classification, Male versus female, 90% and 87% respectively. Table 6 (Box’s M Result) Box’s M statistics was 1.964 with P value 0.000, shows the sample classification coefficients that compose the discriminant function equation i.e. D = -0.122 (ba-ans) + 0.186 (n-ans + 0.410 (ma-wd). Figure 4 (Histograms of discriminant scores: Male and Figure 5: Female) Histograms of discriminant scores showed that male and female have separate distribution of the data. Classification results in Table 7 (classification results) showed, 91.3% males correctly classified in the data, on the basis of discriminant function we made, similarly, 97.1% females were correctly classified in their specific group by the help of this discriminant function. The percentage of skulls correctly classified with this function was 94.2%.

Table 3. Group Statistics According to Sex and in Total
ParametersMaleFemaleTotalP Value
SCo16.1957 ± 3.8244715.7971 ± 3.3936815.9964 ± 3.607830.518
GSgN4.1159 ± 1.292362.0870 ± 0.886823.1014 ± 1.502020.000
SgGM1.1667 ± 0.656790.5580 ± 0.511170.8623 ± 0.661140.000
BaANS99.8986 ± 6.9560594.5942 ± 5.6107097.2464 ± 6.835730.000
BaN10.954 ± 8.2828310.130 ± 5.3914510.542 ± 8.095970.000
NANS63.1739 ± 79.2568349.8406 ± 3.9318656.5072 ± 56.305770.165
NMe12.203 ± 10.8640911.065 ± 7.5026011.634 ± 10.914060.000
FsWd11.5507 ± 2.981749.2319 ± 2.0374810.3913 ± 2.797770.000
FsHt29.6159 ± 6.1286126.5797 ± 7.0926828.0978 ± 6.777450.008
MaSN46.8406 ± 5.4842641.3768 ± 5.4099644.1087 ± 6.080580.000
MaFH31.6594 ± 3.8199328.0725 ± 3.7664129.8659 ± 4.186150.000
MaWd20.3478 ± 3.0040518.5362 ± 3.6444119.4420 ± 3.449360.002
MaHt10.1304 ± 2.268158.7536 ± 2.439909.4420 ± 2.446560.001
ANSMe70.4928 ± 7.1632761.7971 ± 5.6035266.1449 ± 7.752140.000
SL7.6304 ± 1.626197.1304 ± 1.580337.3804 ± 1.617150.069
SW11.3913 ± 1.4774511.0870 ± 1.4115011.2391 ± 1.447650.218
SHtPost8.5870 ± 1.550518.4348 ± 1.576288.5109 ± 1.559600.568
SHtAnt8.9710 ± 1.583198.4783 ± 1.586798.7246 ± 1.598440.070
GSIPI1.0146 ± 0.366440.8407 ± 0.285230.9277 ± 0.338600.002
GPI6.8353 ± 2.341823.3757 ± 1.564515.1055 ± 2.636480.000
ULTc1.0150 ± 0.259981.1611 ± 0.306181.0880 ± 0.292320.003
GMSN98.2754 ± 6.4872889.2754 ± 12.0072193.7754 ± 10.622950.000
GMFH104.84 ± 5.8274898.6957 ± 5.94411101.77 ± 6.625860.000
GMBaN77.5942 ± 5.8191370.5797 ± 6.1223074.0870 ± 6.913940.000
MSgG170.33 ± 6.11331174.04 ± 2.89744172.19 ± 5.116950.000
BaNMe56.1159 ± 3.8292164.2319 ± 6.03066160.1739 ± 4.2767220.267
NMeANS9.4348 ± 3.9685510.2464 ± 3.196539.8406 ± 3.613130.188
SNMe76.7391 ± 4.4113977.0870 ± 4.5559976.9130 ± 4.471280.649
SNAr123.61 ± 5.67299124.39 ± 6.39673124.00 ± 6.036390.448
GNGSg29.3913 ± 7.9209719.3043 ± 6.9075924.3478 ± 8.969250.000
GSGM86.8406 ± 5.7666078.8116 ± 6.0324582.8261 ± 7.127530.000
GSSN11.3478 ± 2.2610911.7681 ± 2.4079811.5580 ± 2.336690.292
SNSMa129.12 ± 5.25395130.36 ± 5.88610129.74 ± 5.593670.192
SGN61.0435 ± 5.2927163.0870 ± 5.2405162.0652 ± 5.346680.024

aValues are expressed as mean ± SD.

Table 4. Wilk’s Lambda
Test of 1Wilk’s LambdaChi-SquaredfP Value
10.278152.320340.000
Table 5. Functions at Group Centroids (Unstandardized Canonical Discriminant Functions Evaluated at Group Means)
GenderFunction 1
Male1.600
Female-1.600
Table 6. Box’s M Result
Box’s M ResultValues
Box’s M1.964
F Approx.2.428
df1595
df25.585
P Value0.000
Histograms of Discriminant Scores: Male
Figure 4. Histograms of Discriminant Scores: Male
Histograms of Discriminant Scores: Female
Figure 5. Histograms of Discriminant Scores: Female
Table 7. Classification Resultsa
Gender (Original )GroupTotal
FemaleFemale
Female67 (97.1)2 (2.9)69 (100.0)
Male6 (8.7)63 (91.3)69 (100.0)

a94.2% of original grouped cases correctly classified.

5. Discussion

It is common knowledge that men regardless of their ethnicity have a larger stature than women, more robust cranial and facial features, along with greater muscularity, strength, and speed (4). Male tooth size exceeds that of female, pre and Post natal hormonal levels differ, growth rates vary, and diseases affect the sex differentially (5).

Rogers (6) reported although the craniofacial growth pattern among the two sexes is essentially the same, sexual dimorphism observed is the result of early attainment of skeletal maturity in women as compared to men. Further, there is variation in the growth of the different parts of the skull, with sexual differences being best defined in late growing structures of the skull, such as lower facial region, facial depth and mastoid process. On the other hand, cranial base and upper face are middle growing regions in which some sexual differences can be evident, but are not likely to be the most distinctive.

In total anterior face height and upper anterior face height, sex differences were highly significant but extreme typological differences appear to override the growth characteristics that are usually attributed to sexual dimorphism (7).

The mastoid region used in this study, being a part of temporal bone, is recognized as being the most protected and resistant to damage, due to its anatomical position at the base of skull, these findings have been reconfirmed by many authors Kloiber, Weels, Gejval, Spence as cited by Wall and Henke (8).

Results of the present study are consistent with these findings as all the linear measurements were greater in males as compared to females.

Results indicate that, Important size-related variables that were captured by the discriminant analysis were anterior face height, upper face height, frontal sinus height, mastoidale to SN plane, mastoidale to Frankhfort plane, and cranial base length. The derived discriminant functional equation in the present study was 82.0% accurate in differentiating the men and women. Franklin et al. (9) , reported an accuracy of 77 to 80% in sexual discrimination using 8 cephalometric variables. Naikmasur et al. (3), claimed accuracy of 81.5% and 88.2% respectively by comparing the reliability of craniomandibular parameters in South Indian and Indian immigrants of Tibetan population using 11 variables on lateral and frontal cephalograms. Hasio et al. (1), studied 100 lateral cephalograms of Taiwanese origin and claimed 100% accuracy in sex determination using 18 cephalometric parameters.

Results on a French sample, where 95.6% accuracy and was achieved by Veyre-Goulet et al. (10). Bigoni et al. (11), also claimed 99% - 100% correct sex classification on a known sex Central European sample using 3 dimensional coordinates.

The variations in the result in different population may be due to the inconsistency in the position of landmarks of the skull in different populations (12). Craniofacial growth like mastoid region, zygomatic process and the ridges of occipital bone are influenced by nutrition, environment and genetic factors (13).

Lateral radiographs are generally available to forensic anthropologists and, as shown in this and other studies, introduce greater gender discriminating accuracy into forensic practice without the need for expensive equipment or computer programs.

5.1. Conclusions

This is an attempt to verify the standard for sex determination based on the lateral cephalometric parameters in Pakistani population. This study was able to attain 94.2% accuracy with thirty four variable model. To obtain higher results further research of the technique with large sample size and both linear and angular cranial dimorphic traits for gender identification is proposed. The proper identification of landmarks, careful measurements and strict statistical methods will yield reliable results and will meet the needs of the forensic investigation in our country.

References

  • 1.

    Hsiao TH, Chang HP, Liu KM. Sex determination by discriminant function analysis of lateral radiographic cephalometry. J Forensic Sci. 1996; 41(5) : 792 -5 [PubMed]

  • 2.

    Zeh JE, Ko D, Krogman BD, Sonntag R. A multinomial model for estimating the size of a whale population from incomplete census data. Biometrics. 1986; 42(1) : 1 -14 [PubMed]

  • 3.

    Naikmasur VG, Shrivastava R, Mutalik S. Determination of sex in South Indians and immigrant Tibetans from cephalometric analysis and discriminant functions. Forensic Sci Int. 2010; 197(1-3) : 1220 -6 [DOI]

  • 4.

    Beach FA. Human Evolution: Biosocial Perspectives. 1978;

  • 5.

    Gilbert RI,, Mielke JH. The Analysis of Prehistoric Diets. 2000;

  • 6.

    Rogers TL. Determining the sex of human remains through cranial morphology. J Forensic Sci. 2005; 50(3) : 493 -500 [PubMed]

  • 7.

    Nanda SK. Patterns of vertical growth in the face. Am J Orthodont Dent Orthoped. 1988; 93(2) : 103 -16 [DOI]

  • 8.

    Keen JA. A study of the differences between male and female skulls. Am J Phys Anthropol. 1950; 8(1) : 65 -80 [DOI]

  • 9.

    Franklin D, Freedman L, Milne N. Sexual dimorphism and discriminant function sexing in indigenous South African crania. Homo. 2005; 55(3) : 213 -28 [PubMed]

  • 10.

    Veyre‐Goulet SA, Mercier C, Robin O, Guerin C. Recent human sexual dimorphism study using cephalometric plots on lateral teleradiography and discriminant function analysis. J Forensic Sci. 2008; 53(4) : 786 -9

  • 11.

    Bigoni L, Veleminska J, Bruzek J. Three-dimensional geometric morphometric analysis of cranio-facial sexual dimorphism in a Central European sample of known sex. Homo. 2010; 61(1) : 16 -32 [DOI][PubMed]

  • 12.

    Glucksmann A. Sexual dimorphism in human and mammalian biology and pathology. 1981;

  • 13.

    Galdames IC, Matamala DAZ, Smith RL. Evaluating accuracy and precision in morphologic traits for sexual dimorphism in malnutrition human skull: a comparative study. Int J Morphol. 2008; 26(4) : 876 -83

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