FORENSIC ANTHROPOLOGY FINAL OUTPUT: FORENSIC ANTHROPOLOGY IN SOUTHEAST ASIA
AGE ESTIMATION
TITLE
STUDY REFERENCE
COUNTRY
RESEARCH FOCUS
Madon, N. F., Lai, P. S., Subramaniam, K., Singh, M. K. C.,
Age estimation by cranial suture using postmortem Faridah, M. O. H. D., & Siew, S. F. (2023). Age estimation
by cranial suture using postmortem computed tomography
computed tomography images among Malaysian
images among Malaysian. Adli Tıp Dergisi, 37(1), 5-11.
Malaysia
Age Estimation
Kurniawan, A., Agitha, S. R. A., Margaretha, M. S., Utomo,
H., Chusida, A. N., Sosiawan, A., ... & Rizky, B. N. (2020).
The applicability of Willems dental age estimation
The applicability of Willems dental age estimation method
method for Indonesian children population in Surabaya
for Indonesian children population in Surabaya. Egyptian
Journal of Forensic Sciences , 10 , 1-4.
Indonesia
Age Estimation
Darmawan, M. F., Abidin, A. F. Z., Kasim, S., Sutikno, T., &
Budiarto, R. (2020). Random forest age estimation model
Random forest age estimation model based on length of
based on length of left hand bone for Asian
left hand bone for asian population
population. International Journal of Electrical and Computer
Engineering , 10 (1), 549.
Asia Region
Age Estimation
Utama, V., Soedarsono, N., & Yuniastuti, M. (2020).
Assessment of agreement between cervical vertebrae Assessment of agreement between cervical vertebrae
skeletal and dental age estimation with chronological skeletal and dental age estimation with chronological age in
an Indonesian population. The Journal of Forensic Odontoage in an Indonesian population
stomatology , 38 (3), 16.
Indonesia
Age Estimation
Ruengdit, S., Prasitwattanaseree, S., Mekjaidee, K.,
Sinthubua, A., & Mahakkanukrauh, P. (2018). Age
Age estimation approaches using cranial suture closure:
estimation approaches using cranial suture closure: A
A validation study on a Thai population
validation study on a Thai population. Journal of forensic
and legal medicine , 53 , 79-86.
Age estimation methods using hand and
radiographs in a group of contemporary Thais
Benjavongkulchai, S., & Pittayapat, P. (2018). Age
wrist estimation methods using hand and wrist radiographs in a
group
of
contemporary
Thais. Forensic
science
international , 287 , 218-e1.
Understanding Population-Specific Age Estimation Using Kim, J. E. (2016). Understanding population-specific age
Multivariate Cumulative Probit Regression For Asian estimation using multivariate cumulative probit regression for
Asian skeletal samples.
Skeletal Samples
Dental age estimation of growing children
measurement of open apices: A Malaysian formula
Cugati, N., Kumaresan, R., Srinivasan, B., & Karthikeyan, P.
by (2015). Dental age estimation of growing children by
measurement of open apices: A Malaysian formula. Journal
of forensic dental sciences , 7 (3), 227.
Thailand
Thailand
Thailand
Malaysia
Age Estimation
Age Estimation
Age Estimation
Age Estimation
Kampan, N., Sinthubua, A., & Mahakkanukrauh, P. (2014).
A New Method for Age Estimation from Ectocranial
A New Method for Age Estimation from Ectocranial Suture
Suture Closure in a Thai Population
Closure in a Thai Population. Siriraj Medical Journal , 66 (3).
Thailand
Age Estimation
Nik-Hussein, N. N., Kee, K. M., & Gan, P. (2011). Validity of
Validity of Demirjian and Willems methods for dental age Demirjian and Willems methods for dental age estimation for
Malaysian children aged 5–15 years old. Forensic science
estimation for Malaysian children aged 5–15 years old
international , 204 (1-3), 208-e1.
Malaysia
Age Estimation
METHODOLOGY
KEY FINDINGS
SAMPLE SIZE
There is no discernible bias in the age prediction due to ethnicity or sex.
After meeting the inclusion and exclusion criteria, post-mortem
Age and the overall scores of the cranial suture closure pattern showed
computed tomography (PMCT) scans from 2015 to 2018 were
a strong association. The findings demonstrated the value of the
analyzed. The Meindl and Lovejoy (ML) scoring system was utilized
machine learning (ML) scoring approach for age estimation, with 62.3%
to determine the approximate age. The association between age
of instances successfully predicted within both the ML reference age 106 samples
and the sum scores of seven distinct suture sites was examined,
range and the standard of estimation error (SEE) range. Using the vault
and the regression formula was utilized to conduct a crosssystem and the machine learning approach on PMCT DICOM images of
validation research with respect to real age, accounting for bias and
the cranium, there was a rather strong correlation found between the
inaccuracy.
age and the pattern of cranial suture closure.
In this study, the mean chronological age (CA) for males is
11.30 ± 1.43 years, while for girls it is 11.65 ± 1.55 years. For both boys
and girls, the total mean difference between CA and EDA is −
The Willems approach was utilized by two examiners who were
0.08 ± 0.76 and − 0.31 ± 0.97, respectively. The mean age difference in 110 panoramic radiographs samples
blinded to determine the estimated dental age (EDA). IBM®
females was more substantial than in boys, according to this study. The of the patients aged 6 to 14 years
SPSS® Statistics version 23.0 was utilized for the statistical
timing of the pubertal growth spurt in girls begins around two years old.
analysis (IBM, Armonk, NY, USA).
sooner than in boys, which may account for differences in dental
maturation between the sexes. Girls often begin and complete their
dental development earlier than boys do.
The RF model has equivalent performance to the ANN and SVM
The estimate model is produced using a single soft computing models, according to the findings generated. The RF model performs
model, Random Forest (RF), and the outcomes are contrasted with better than the ANN but less well than the SVM model for male
Asian people' left hand bone lengths
those of the Artificial Neural Network (ANN) and Support Vector individuals. Regarding female individuals, both the ANN and SVM
span from birth to eighteen years
Machine (SVM) models created in earlier case studies. R-square models are outperformed by the RF model. All things considered, when
old.
and the Mean Square Error (MSE) value were the performance it comes to estimating age for female individuals, the RF model
metrics utilized in both this investigation and the earlier case study. outperforms the ANN and SVM models; on the other hand, for male
subjects, the RF model comes in second place.
A mean difference of -0.094 ± 1.52 years was seen in the Bland-Altman
plot of the skeletal and dental ages of the cervical vertebrae, with upper
and lower limits of 2.882 and -3.070 years, respectively. The averages
In addition to estimating the dental age and assessing agreement for the skeletal, dental, and chronological ages of the cervical vertebrae
100
lateral
cephalometric
between
the
cervical vertebrae
skeletal-dental, dental- were 13.97 (2.67), 14.06 (2.45), and 13.97 (2.97), in that order. The
radiographs,
and
then
100
chronological, and skeletal-chronological ages, the goal of this mean discrepancies between the dental- and skeletal-chronological
panoramic
radiographs
of
study is to develop a formula to estimate the skeletal age of ages of the cervical vertebrae were 4.005 (2.07) and 0.566 (2.26),
participants aged 9 to 18 were used
cervical vertebrae using multiple linear regression analyses. respectively. Furthermore, the accuracy of the cervical vertebrae
to calculate dental tooth crown index
Cervical vertebrae ratios were obtained by measuring a number of skeletal age estimate formula employing successive sampling was
data.
anatomical factors.
tested in a validation study (group 2, n = 10; three males and seven
females). There was a 9–11 age range. Compared to dental age, the
skeletal age of the cervical vertebrae exhibited a stronger correlation
with chronological age.
With an error range of around 13–22 years, bias and inaccuracy in the
Meindl and Lovejoy, Acsádi and Nemeskéri, and Mann techniques led to
overestimation in young people and underestimating in older persons.
The Mann technique performed poorly on Thai ladies, but it was nearly
Meindl and Lovejoy (1985), Acsádi and Nemeskéri (1970), and
100% accurate in estimating age in older guys. The findings verify the 175 samples of dry crania
Mann (1991) methods were applied in this research.
existence of inter-population variance. Furthermore, there might be a
rise in inaccuracy due to age variations between Thais and the groups
utilized to design the methodologies. These three anti-aging strategies,
according to this study, should be used with other approaches.
All three techniques' predicted ages differed markedly from the
chronological age, with the exception of Tanner-Whitehouse 3 RUS in
men. Greulich-Pyle demonstrated the highest accuracy (83.2% for
The radiographs were obtained from 2011 to 2016. For every females and 79.63% for men) when it came to legal age criteria, with a
radiograph, the Greulich–Pyle, Tanner–Whitehouse 3, ulna, and 10 year old threshold. The legal age thresholds of 13 (77.5% for
selected short bones (RUS) methods, as well as the Fishman females and 74.31% for males) and 15 (83.08% for females and
technique, were used. We used the Wilcoxon signed ranks test 73.77% for males) were most accurately determined using the Fishman
365 hand and wrist radiographs of 8
with Bonferroni correction to compare the predicted ages from each approach. Greulich-Pyle's accuracy for the age threshold of eighteen
to 20 years old Thai patients
approach with the chronological age. Testing was done on the key years was 53.85% for females and 54.44% for males. Reliability testing
legal age criteria in Thailand (10, 13, 15, and 18 years old) for revealed strong to near-perfect agreement. For male individuals who
sensitivity, specificity, and accuracy. Weighted kappa analysis was were current Thai children and adolescents, Tanner–Whitehouse 3 RUS
used to assess intra- and inter-observer reliability.
age did not significantly differ from chronological age in this
investigation. On the basis of Thai legal age thresholds, Greulich-Pyle
and Fishman technique performed better in terms of prediction
accuracy.
The study's findings demonstrate that, contrary to popular belief, age
estimation methods tailored to the Japanese population do not always
Four age estimating techniques were used: the "conventional" result in higher age estimations for Thai people. Therefore, age
techniques of Suchey and Katz (1998), Lovejoy et al. (1985a), and estimations were dramatically improved when multivariate ordered probit
Meindl and Lovejoy (1985), in addition to Transition Analysis (TA) models were fitted to a pooled sample of Thai and Japanese individuals. Four skeleton collections depicting
(Boldsen et al. 2002b). A multivariate ordered probit regression For Japanese and Thai populations, both TA and traditional approaches contemporary Thais and Japanese
model was fitted to the Asian skeletal data inside a Bayesian produced appropriate age estimates; however, the probit models of from the 20th century
framework in order to build age estimate models for the Asian pooled Asian groups fared better than other methods. When the scores
samples while reducing methodological error.
from traditional approaches and TA are combined to construct a
multivariate ordered probit model, the findings become much more
encouraging.
All three ethnic origins' orthopantomographs were digitalized and
Cameriere's age estimate method was applied to examine the data. The regression model was fitted using the factors that considerably
The number of teeth with full root development (N0), gender (g), improved the fit, and the result was the formula Age = 11.368-0.345g + 421 Malaysian children aged
ethnicity, sum of normalized open apices (s), age of the patients 0.553No -1.096s - 0.380s.No, where g is a variable and is 1 for men and between 5 and 16 years
was modeled as a function of these morphological factors as well 2 for females. 87.1% of the total deviation was explained by the formula.
as the first-order interaction between s and N0.
The coronal, sagittal, and lambdoid sutures on the ectocranial
surface were known as craniofacial sutures. Two methods were Only when assessed via tracing, the results indicated a substantial
used to record the obliteration of each suture in order to evaluate negative connection between age and the number of pixels per
the closure: tracing and photography. Using ImageJ software, the centimeter in the coronal suture. Age = 76.872 – (19.609 x the number
100 Thai skulls aged 15 to 96 years
pixels of the surviving sutures were counted to determine the extent of pixels per centimeter in the coronal suture from tracing) + (3.710 x the
old
of suture obliteration. Each suture's length was measured in number of pixels per centimeter in the lambdoid suture from tracing) was
centimeters, and at the 0.05 significance level, Pearson's a predictive model created using stepwise linear regression in this study.
correlations were used to assess each suture's pixel count per It had a standard error of ±13.9 years.
centimeter.
For both genders, the sample's mean chronological ages were 10.1 ±
2.8 and 9.9 ± 3.0 years, respectively. The mean estimated dental age
The method used in this research is the mean Demirjian and for boys and females, respectively, using the Demirjian approach was 991 dental panoramic radiographs
Willems estimated ages were compared to the mean chronological 10.8 ± 2.9 years and 10.5 ± 2.9 years. The mean estimated age for men of 5 to 15 years old Malaysian
age.
using the Willems approach was 10.3 ± 2.8 years, while for females it children
was 10.0 ± 3.0 years. Therefore, the Willems approach was better
appropriate for determining a child's dental age in Malaysia.
YEAR OF PUBLICATION
NOTES
2023
N/A
2020
Applying this strategy to the 11–14-yearold female population in Surabaya may
need a thorough investigation because
there was a significant underestimating
in this group.
2020
N/A
2020
N/A
2018
Improved age estimation findings may
come from more study that creates
techniques specifically tailored for Thai
people.
2018
Further research should be done on
adapting age estimate techniques
particularly for the modern Thai
population,
given
the
potential
consequences of secular changes and
ethnic differences.
2016
The finding has broader ramifications in
that a single age estimate approach can
be established, at least for Thais and
Japanese. Furthermore, the calculation
of ages at death may contain bias and
error that is not attributable to variations
in the population-specificity of skeletal
aging, and the error in age estimation
resulting
from
biased
reference
samples may be larger than anticipated.
2015
The results show that the original
Cameriere formula has to be reframed
to better fit the needs of the country's
people. To assess this formula's
applicability on a bigger sample size,
more research has to be done.
2014
N/A
2011
N/A