DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks

A web application for sex and stature estimation from radiographic proximal femur for a Thai population

In both forensic and archaeological domains, the discovery of incomplete human remains is a frequent occurrence. Nevertheless, the estimation of biological profiles from such remains presents a challenge due to the absence of crucial skeletal elements, such as the skull and pelvis. This study aimed to assess the utility of the proximal femur in the forensic identification process by creating a web application for osteometric analysis of the proximal femur. The aim was to determine the sex and stature of an individual from radiographs of the left anteroposterior femur. To accomplish this, an automated method was developed for acquiring linear measurements from radiographic images of the proximal femur using Python tools. The application of Hough techniques and Canny edge detection was utilized to generate linear femoral dimensions from radiographs. A total of 354 left femora were radiographed and measured by the algorithm. The sex classification model employed in this study was the Naïve Bayes algorithm (accuracy = 91.2 %). Results indicated that Gaussian process regression (GPR) was the most effective method for estimating stature (mean error = 4.68 cm, SD = 3.93 cm). The proposed web application holds the potential to serve as a valuable asset in the realm of forensic investigations in Thailand, particularly in the estimation of biological profiles from fragmentary skeletal remains.

Automatic Segmentation of Osteonal Microstructure in Human Cortical Bone Using Deep Learning: A Proof of Concept

Cortical bone microstructure assessment in biological and forensic anthropology can assist with the estimation of age-at-death and animal-human differentiation, for example. Osteonal structures within cortical bone are the key feature under analysis, with osteon frequency and metric parameters providing crucial information for the assessment. Currently, the histomorphological assessment consists of a time-consuming manual process for which specific training is required. Our work investigates the feasibility of automatic analysis of human bone microstructure images through the application of deep learning. In this paper, we use a U-Net architecture to address the semantic segmentation of such images into three classes: intact osteons, fragmentary osteons, and background. Data augmentation was used to avoid overfitting. We evaluated our fully automatic approach using a sample of 99 microphotographs. The contours of intact and fragmentary osteons were traced manually to provide ground truth. The Dice coefficients were 0.73 for intact osteons, 0.38 for fragmented osteons, and 0.81 for background, giving an average of 0.64. The Dice coefficient of the binary classification osteon-background was 0.82. Although further refinement of the initial model and tests with larger datasets are needed, this study provides, to the best of our knowledge, the first proof of concept for the use of computer vision and deep learning for differentiating both intact and fragmentary osteons in human cortical bone. This approach has the potential to widen and facilitate the use of histomorphological assessment in the biological and forensic anthropology communities.

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

Currently, bone age is assessed by X-rays. It enables the evaluation of the child's development and is an important diagnostic factor. However, it is not sufficient to diagnose a specific disease because the diagnoses and prognoses may arise depending on how much the given case differs from the norms of bone age.

BACKGROUND

The use of magnetic resonance images (MRI) to assess the age of the patient would extend diagnostic possibilities. The bone age test could then become a routine screening test. Changing the method of determining the bone age would also prevent the patient from taking a dose of ionizing radiation, making the test less invasive.

METHODS

The regions of interest containing the wrist area and the epiphyses of the radius are marked on the magnetic resonance imaging of the non-dominant hand of boys aged 9 to 17 years. Textural features are computed for these regions, as it is assumed that the texture of the wrist image contains information about bone age.

RESULTS

The regression analysis revealed that there is a high correlation between the bone age of a patient and the MRI-derived textural features derived from MRI. For DICOM T1-weighted data, the best scores reached 0.94 R2, 0.46 RMSE, 0.21 MSE, and 0.33 MAE.

CONCLUSIONS

The experiments performed have shown that using the MRI images gives reliable results in the assessment of bone age while not exposing the patient to ionizing radiation.

Convolutional neural network of age-related trends digital radiographs of medial clavicle in a Thai population: a preliminary study

Age at death estimation has always been a crucial yet challenging part of identification process in forensic field. The use of human skeletons have long been explored using the principle of macro and micro-architecture change in correlation with increasing age. The clavicle is recommended as the best candidate for accurate age estimation because of its accessibility, time to maturation and minimal effect from weight. Our study applies pre-trained convolutional neural network in order to achieve the most accurate and cost effective age estimation model using clavicular bone. The total of 988 clavicles of Thai population with known age and sex were radiographed using Kodak 9000 Extra-oral Imaging System. The radiographs then went through preprocessing protocol which include region of interest selection and quality assessment. Additional samples were generated using generative adversarial network. The total clavicular images used in this study were 3,999 which were then separated into training and test set, and the test set were subsequently categorized into 7 age groups. GoogLeNet was modified at two layers and fine tuned the parameters. The highest validation accuracy was 89.02% but the test set achieved only 30% accuracy. Our results show that the use of medial clavicular radiographs has a potential in the field of age at death estimation, thus, further study is recommended.

A machine-learning approach using pubic CT based on radiomics to estimate adult ages

PURPOSE

Adult skeletal age estimation is an active research field. To evaluate the performance of a pubic CT radiomics-based machine learning model for estimating age, we established a multiple linear regression model based on radiomics and machine learning methods.

METHODS

A total of 355 subjects were enrolled in this retrospective study from August 2016 to August 2021, and divided into a training cohort (N = 325) and a testing cohort (N = 30). Computerized texture analysis of the semi-automatically segmentation was performed and 107 texture features were extracted from the regions. Then we used univariate linear regression and multivariate stepwise regression to assess correlations of texture parameters with age. The most vital features were used to make the best predictive model. Eventually, the established radiomics model was tested with an additional 30 patients.

RESULTS

Clinical characteristics include age, sex, height, weight and BMI were not statistically significant different between training and testing cohort (p = 0.098-0.888). Through a multivariate regression analysis using stepwise regression, six texture parameters were found to have significant correlations with age. The regression formula estimating the age was constructed.

CONCLUSIONS

The radiomics model using machine learning is considered as a new approach forage estimation frompubic symphysis CT features.Digital osteology is obtained in a non-invasive way so that it can be an ideal collection for anthropological studies.

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

Age prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this study, we used low-resolution, dual-energy, full-body X-ray absorptiometry images to train deep learning models to predict age. In particular, we proposed a preprocessing framework and adapted many partially pretrained convolutional neural network (CNN) models to predict the age of children and young adults. We used a new dataset of 910 multispectral images that were weakly annotated by specialists. The experimental results showed that the proposed preprocessing techniques and the adapted approach to the CNN model achieved a discrepancy between chronological age and predicted age of around 15.56 months for low-resolution whole-body X-rays. Furthermore, we found that the main factor that influenced age prediction scores was spatial features, not multispectral features.

The computational age-at-death estimation from 3D surface models of the adult pubic symphysis using data mining methods

Age-at-death estimation of adult skeletal remains is a key part of biological profile estimation, yet it remains problematic for several reasons. One of them may be the subjective nature of the evaluation of age-related changes, or the fact that the human eye is unable to detect all the relevant surface changes. We have several aims: (1) to validate already existing computer models for age estimation; (2) to propose our own expert system based on computational approaches to eliminate the factor of subjectivity and to use the full potential of surface changes on an articulation area; and (3) to determine what age range the pubic symphysis is useful for age estimation. A sample of 483 3D representations of the pubic symphyseal surfaces from the ossa coxae of adult individuals coming from four European (two from Portugal, one from Switzerland and Greece) and one Asian (Thailand) identified skeletal collections was used. A validation of published algorithms showed very high error in our dataset-the Mean Absolute Error (MAE) ranged from 16.2 and 25.1 years. Two completely new approaches were proposed in this paper: SASS (Simple Automated Symphyseal Surface-based) and AANNESS (Advanced Automated Neural Network-grounded Extended Symphyseal Surface-based), whose MAE values are 11.7 and 10.6 years, respectively. Lastly, it was demonstrated that our models could estimate the age-at-death using the pubic symphysis over the entire adult age range. The proposed models offer objective age estimates with low estimation error (compared to traditional visual methods) and are able to estimate age using the pubic symphysis across the entire adult age range.

Estimating the age at death for forensic cases using quantitative computed tomography

Estimation of the age at death is an important task for forensic scientists. Although the correlation between age and bone mineral density is already known, including for cadavers, to our knowledge, there are no published studies on age estimation with quantitative computed tomography. Quantitative computed tomography can be used to measure bone mineral density based on the mean computed tomography value of the cancellous bone. As this value cannot be calculated in putrefied cases, we modified quantitative computed tomography to calculate the bone mineral density from regions of the bone with mean computed tomography values of 50-350 Hounsfield units. We aimed to examine whether this method could be used for age estimation. We examined 171 male and 106 female cadavers, some of which were putrefied. We performed univariate linear regression analysis for age at death and bone mineral density. The resultant intercept, slope, and root mean square error were 91.3, - 0.20 (p < 0.0001), and 11.4, respectively, for male cadavers, and 96.1, - 0.23 (p < 0.0001), and 11.0, respectively, for female cadavers. We evaluated this regression formula by using 10-fold cross-validation, resulting in a coefficient of determination of 0.33 for male cadavers and 0.42 for female cadavers. The modified quantitative computed tomography method may be of assistance in estimating age at death, even in putrefied cases.

DXAGE 2.0 - adult age at death estimation using bone loss in the proximal femur and the second metacarpal

The accurate age at death assessment of unidentified adult skeletal individuals is a critical research task in forensic anthropology, being a key feature for the determination of biological profiles of individual skeletal remains. We have previously shown that the age-related decrease of bone mineral density (BMD) in the proximal femur could be used to assess age at death in women (Navega et al., J Forensic Sci 63:497-503, 2018). The present study aims to generate models for age estimation in both sexes through bone densitometry of the femur and radiogrammetry of the second metacarpal. The training sample comprised 224 adults (120 females, 104 males) from the "Coimbra Identified Skeletal Collection," and different models were generated through least squares regression and general regression neural networks (GRNN). The models were operationalized in a user-friendly online interface at https://osteomics.com/DXAGE2/ . The mean absolute difference between the known and estimated age at death ranges from 9.39 to 13.18 years among women and from 10.33 to 15.76 among men with the least squares regression models. For the GRNN models, the mean absolute difference between documented and projected age ranges from 8.44 to 12.58 years in women and from 10.56 to 16.18 years in men. DXAGE 2.0 enables age estimation in incomplete and/or fragmentary skeletal remains, using alternative skeletal regions, with reliable results.

Adult Skeletal Age-at-Death Estimation through Deep Random Neural Networks: A New Method and Its Computational Analysis

Age-at-death assessment is a crucial step in the identification process of skeletal human remains. Nonetheless, in adult individuals this task is particularly difficult to achieve with reasonable accuracy due to high variability in the senescence processes. To improve the accuracy of age-at-estimation, in this work we propose a new method based on a multifactorial macroscopic analysis and deep random neural network models. A sample of 500 identified skeletons was used to establish a reference dataset (age-at-death: 19–101 years old, 250 males and 250 females). A total of 64 skeletal traits are covered in the proposed macroscopic technique. Age-at-death estimation is tackled from a function approximation perspective and a regression approach is used to infer both point and prediction interval estimates. Based on cross-validation and computational experiments, our results demonstrate that age estimation from skeletal remains can be accurately (~6 years mean absolute error) inferred across the entire adult age span and informative estimates and prediction intervals can be obtained for the elderly population. A novel software tool, DRNNAGE, was made available to the community.

Contribution of the use of clavicle bone density in age estimation

Age estimation is an essential element in the field of forensics and is also of judicial interest in forensic medicine of the living. Despite all the methods, age estimation is often imprecise with many biases, especially in mature individuals. The main objective of our study is to assess the correlation between age and computed tomography bone mineral density of the medial end of the clavicle. A sample of 180 chest multi-slice computed tomography scans without contrast injection performed in living individuals, with suspected respiratory infection, aged 15 to 100 years was used. Bone density measurement was performed on the medial meta-epiphyseal region of the clavicles. A significant negative correlation was found between bone density and age of individuals (p-value < 0.05). The mean absolute error was calculated for men at 13.4 years and 13.1 years for women, which was associated with an absence of bias. Good precision of the estimate for both sexes was also calculated on a subgroup of individuals whose age was greater than 40 years, with average absolute errors of about 12 years, which was associated with an absence of relative error. In view of our preliminary results, the study of bone density of the medial end of the clavicle could be of interest in the estimation of age. Several studies will be necessary to evaluate the reproducibility of these results on independent bone samples and in the estimation of age in the living individual.

Elemental Composition in Female Dry Femora Using Portable X-Ray Fluorescence (pXRF): Association with Age and Osteoporosis

Pathophysiological conditions can modify the skeletal chemical concentration. This study analyzes the elemental composition in two anatomical regions from dry femoral bone using a portable X-Ray Fluorescence (pXRF) and evaluates its impact in the bone mineral density (BMD). The left femora of 97 female skeletons (21-95 years old individuals) from the Coimbra Identified Skeletal Collection were studied. Diagenetic biases were discarded at the outset and BMD was determined with Dual-energy X-ray absorptiometry. Chemical measurements were performed at the midpoint of the femoral neck and at the midshaft using a pXRF device, and comparisons were made considering the age and the BMD values. Only elements with a Technical Measurement Error ≤ 5% were selected: P, S, Ca, Fe, Zn, As, Sr, Pb and the Ca/P ratio. Statistically significant differences were found between regions, with higher concentrations of P, Ca, Zn and S at the midshaft, and the Ca/P ratio at the femoral neck. The concentration of P is higher in individuals < 50 years, while S and Ca/P ratio increase in individuals ≥ 50 years. The decrease of P with age can be simultaneously related to the decline of its concentration in osteoporosis. Decreased BMD is also associated with higher levels of S and Pb. Osteoporosis enhances the absorption of osteolytic elements in specific locations. This fast and non-destructive technique has proved effective for the comprehension of chemical changes related to bone mass loss. This study highlights the potential of identified skeletal collections to improve the knowledge about bone fragility.

Age estimation based on computed tomography exploration: a combined method

Despite an extensive number of existing methods, age estimation of human remains is still an unsolved matter in the field of forensic anthropology, especially when it comes to mature adults. The specific aim of this work was to propose a combined method for age estimation, for forensic purposes, by coupling the Suchey-Brooks method and the measure of the pubic bone density. For this purpose, we used an independent test sample comprising 339 CT scans of living individuals aged 15 to 99 years old. Measurement of bone density and staging according to the Suchey-Brooks phases were performed, followed by estimation of ages based on a combined method and an existing virtual reference sample. Results highlighted a significant negative correlation between bone density and age. Good accuracy was obtained for the measurement of pubic bone density for age estimation of men and women, especially concerning mature adults, with an absolute error ranging from 9 to 16 years for all individuals. The authors propose a practical combined method consisting of, first, allocating phases according to the scannographic approach of the Suchey-Brooks method. For phases I to IV, the age estimation is given using the Suchey-Brooks method. For phases V to VI, the pubic bone density measurement is used. Further study will be needed to assess the reproducibility of these results on cadavers and dry bones, as the post-mortem process could interfere with the measurement of mineral bone density.

Age related changes of rib cortical bone matrix and the application to forensic age-at-death estimation

Forensic anthropology includes, amongst other applications, the positive identification of unknown human skeletal remains. The first step in this process is an assessment of the biological profile, that is: sex, age, stature and ancestry. In forensic contexts, age estimation is one of the main challenges in the process of identification. Recently established admissibility criteria are driving researchers towards standardisation of methodological procedures. Despite these changes, experience still plays a central role in anthropological examinations. In order to avoid this issue, age estimation procedures (i) must be presented to the scientific community and published in peer reviewed journals, (ii) accurately explained in terms of procedure and (iii) present clear information about the accuracy of the estimation and possible error rates. In order to fulfil all these requirements, a number of methods based on physiological processes which result in biochemical changes in various tissue structures at the molecular level, such as modifications in DNA-methylation and telomere shortening, racemization of proteins and stable isotopes analysis, have been developed. The current work proposes a new systematic approach in age estimation based on tracing physicochemical and mechanical degeneration of the rib cortical bone matrix. This study used autopsy material from 113 rib specimens. A set of 33 parameters were measured by standard bio-mechanical (nanoindentation and microindentation), physical (TGA/DSC, XRD and FTIR) and histomorphometry (porosity-ImageJ) methods. Stepwise regressions were used to create equations that would produce the best 'estimates of age at death' vs real age of the cadavers. Five equations were produced; in the best of cases an equation counting 7 parameters had an R² = 0.863 and mean absolute error of 4.64 years. The present method meets all the admissibility criteria previously described. Furthermore, the method is experience-independent and as such can be performed without previous expert knowledge of forensic anthropology and human anatomy.

Estimation of Cadaveric Age by Ultrasonography

(1) Background: While decreasing bone mineral density (BMD) with age in living people has been well documented, a correlation between age and bone mineral density in deceased people has only been reported in a few studies. A correlation between age and BMD in deceased people was investigated as an objective tool for age estimation of unidentified remains. (2) Methods: The Bone Area Ratio (BAR) was measured in 402 autopsy cases (143 females and 259 males over the age of 20). (3) Results: The correlation coefficient in the females was r = -0.5476, and the correlation coefficient in the males was r = -0.2166, indicating a stronger correlation in females than in males. A comparison of the BAR values in the deceased females for each age group with that in live females found no significant differences in the BAR values. BAR values in the deceased were similar to in live individuals, and this did not change with duration of the postmortem interval. (4) Conclusions: Measuring the BAR value based on bone mass using ultrasonic waves is rapid and easy, even for those lacking forensic training, and may be used to estimate the age of an individual and the likelihood of fracture due to trauma.

Technical note: age estimation by using pubic bone densitometry according to a twofold mode of CT measurement

In forensic anthropology, age estimation is a major element in the determination of a biological profile and the identification of individuals. Thus, many anatomical structures have been studied, such as the pubic symphysis, which is a source of major interest due to its late maturation. One of the most well-known methods of assessment is the Suchey-Brooks (SB) system based on the morphological characteristics of the pubic symphysis. The aim of this study was to propose linear regression formulae in order to deduce chronological age from bone density, using both Hounsfield unit (HU), and mean bone density (mBD) values of the pubic symphysis. Moreover, we intended to test the reliability and then to explore the feasibility of using HU instead of mBD values for age estimation. We built retrospectively a reference sample of 400 pubic symphyses using computed tomography at a French hospital and a test sample of 120 pubic symphyses. Equations were created to establish linear regression models for age estimation. Inaccuracy and bias were calculated for individuals aged more or less than 40 years. We highlighted homogeneous mean absolute errors for both HU and mBD values, most of them being less than 10 years. Moreover, we reported a moderate overestimation for younger individuals and a very small underestimation for older individuals. This study proposes a correlation between the bone density and age of individuals with a valuable level of reliability. Finally, HU measurements seem to be suitable for linking bone density with the age of individuals in forensic practice.

Deep biomarkers of aging and longevity: from research to applications

Multiple recent advances in machine learning enabled computer systems to exceed human performance in many tasks including voice, text, and speech recognition and complex strategy games. Aging is a complex multifactorial process driven by and resulting in the many minute changes transpiring at every level of the human organism. Deep learning systems trained on the many measurable features changing in time can generalize and learn the many biological processes on the population and individual levels. The deep age predictors can help advance aging research by establishing causal relationships in non-linear systems. Deep aging clocks can be used for identification of novel therapeutic targets, evaluating the efficacy of the various interventions, data quality control, data economics, prediction of health trajectories, mortality, and many other applications. Here we present the current state of development of the deep aging clocks in the context of the pharmaceutical research and development and clinical applications.

CADOES: An interactive machine-learning approach for sex estimation with the pelvis

The pelvis is consistently regarded as the most sexually dimorphic region of the human skeleton, and methods for sex estimation with the pelvic bones are usually very accurate. In this investigation, population-specific osteometric models for the assessment of sex with the pelvis were designed using a dataset provided by J.A. Serra (1938) that included 256 individuals (131 females and 125 males) from the Coimbra Identified Skeletal Collection and 38 metric variables. The models for sex estimation were operationalized through an online application and decision support system, CADOES. Different classification algorithms generated high accuracy models, ranging from 85% to 92%, with only three variables; and from 85.33% to 97.33%, with all 38 variables. CADOES conveys a probabilistic prediction of skeletal sex, as well as a suite of attributes with educational applicability in the fields of human skeletal anatomy and statistics. This study upholds the value of the pelvis for the estimation of skeletal sex and provides models for that can be applied with high accuracy and low bias.

Outline Shape Analysis on the Trochlear Constriction and Olecranon Fossa of the Humerus: Insights for Sex Estimation and a New Computational Tool

Sex estimation through visual analysis of the distal humerus can contribute to establishing the biological profile of an unidentified skeletal individual. Using statistical shape modeling, the trochlear constriction open curves and olecranon fossa closed outlines of 151 humeri were digitized and analyzed. The shape configurations exhibited strikingly different degrees of sexual dimorphism when evaluated using linear discriminant analysis with leave-one-out cross-validation. The trochlear constriction performed poorly, correctly classifying 63.6% of the individuals. However, the olecranon fossa showed high sexual dimorphism, presenting a 94.0% accuracy. A simpler model using only two principal components was also generated. While the accuracy is slightly inferior (88.1%), it has the advantage of being constrained to bidimensional components that were translated into morphoscopic variables within a simulator interface. This allowed us to implement the method through a web application that does not require users to be trained in landmark digitization or have knowledge of geometric morphometrics.

Estimation of age in forensic anthropology: historical perspective and recent methodological advances

Estimation of age represents a central focus of forensic anthropological analysis of human skeletal remains and of the living. Advances registered in recent research include the topics of taphonomic impact, new anatomical areas of interest, histology, population variation, the dental pulp chamber, technology, mathematical approaches, biochemical analysis and techniques specifically targeting the living. This article reviews the historical development of age estimation methods and considers likely future directions.

Correlation between pubic bone mineral density and age from a computed tomography sample

In forensic anthropology, the estimation of age at death is mainly required to establish a biological profile and facilitate individual identification. The Suchey-Brooks (SB) system represents one of the most commonly used and tested methods of assessment based on the morphological characteristics of the pubic symphysis. However, this method has certain drawbacks, including frequently inaccurate estimation for older individuals. The aim of this work was to test the evolution of pubic bone mineral density (BMD) according to age, especially for individuals over 40 years old. We retrospectively studied pubic bones from males (n = 88) and females (n = 113) over 40 years of age undergoing clinical multi-slice computed tomography (MSCT) in a French hospital between November 2017 and April 2018. The results revealed a significant negative correlation between BMD and age for males (R = -0.62) and females (R = -0.55). The intra- and inter-observer reliabilities of the phase allocation were moderate for males (kappa values at 0.72 and 0.65) and strong for females (kappa values at 0.93 and 0.86). Moreover, a significant difference in BMD between stages 6-1 and 6-2 for males was observed. We hypothesize that BMD might help improve the reliability of the SB system for older individuals. In addition to the pubic bone, numerous other anatomical regions such as the area of Ward could represent interesting areas of study in order to relate bone density to age.

Age estimation using bone mineral density in South Africans

The use of bone mineral density (BMD) to predict age-at-death in skeletal remains provides a usable alternative to other methods because the values obtained are not observer-dependent. The aim of this study was to investigate the usability of BMD to estimate age in South African populations, and to assess inter-population variation and sex-specific differences in BMD values from the proximal end of the femur. In order to estimate age, regression analysis was done for the construction of population dependent formulae. The sample comprised of a total of 123 femora of black and white South Africans. DXA scans were performed using the Hologic Discovery system. Data analysis was done by employing independent-samples t-tests and correlation/regression analyses. The results indicated a statistically significant difference between black and white South Africans. Male groups were also significantly different from one another, but black and white females showed no significant differences. Linear regression analysis revealed a significant correlation between BMD values and age for the white population and the combined sample, but not for the black population. Bootstrapping were employed to confirm validity of the results. In conclusion, this study showed that the use of DXA measurements of the femur for estimating age may be used for the estimation of age-at-death in white South Africans, but more research is needed to better understand the relationship between bone mineral density and age in black South Africans.

Bone Mineral Density Adult Age Estimation in Forensic Anthropology: A Test of the DXAGE Application

Estimating age-at-death of individuals represented only by skeletonized human remains is a fundamental aspect of forensic anthropological casework. Recently, several researchers have proposed that bone mineral density (BMD) is a useful predictor of age-at-death in forensic contexts. Navega et al. (JFS 63(2):497-503) developed an online application called DXAGE for calculating age-at-death from BMD parameters. This study tests the utility of DXAGE by utilizing data from the National Health and Nutrition Examination Survey (NHANES). BMD data from a female subsample (n = 470) of the NHANES 2007-2008 dataset were analyzed, and the relationship between predicted age and real age was examined. Inaccuracy was 14.25 years, and bias was -7.20 years. Results show that there is a weak correlation between predicted and actual age (r = 0.47) using the DXAGE application. While BMD data are potentially useful for predicting age age-at-death, the DXAGE application should be used cautiously in forensic anthropological contexts.

A test and analysis of Calce (2012) method for skeletal age-at-death estimation using the acetabulum in a modern skeletal sample

In forensic anthropology, the age-at-death of an adult individual is one of the most complex parameters of the biological profile to estimate. The present study aims to evaluate the reliability of the Calce (2012) method for the estimation of age-at-death through acetabulum changes in a sample of Portuguese origin. This method consists of the global analysis of acetabular age-related morphology with focus on three specific traits, namely the acetabular groove, the osteophyte development of the acetabular rim, and the apex growth. This method was tested in 120 individuals sampled from the Twenty-first Century Identified Skeletal Collection (University of Coimbra, Portugal). The test sample is composed of 60 males and 60 females, aged between 25 and 99 years, with well-preserved os coxae. The results showed that only 60% of the individuals were correctly attributed to the age group defined by the technique. The comparison with previous studies in other populations shows significant inter-population differences in the relationship between the acetabulum variables used by Calce and age-at-death. The obtained results advise caution in the use of the Calce (2012) method to estimate the age-at-death of unidentified skeletons.