Forensic sex estimation using the vertebrae: an evaluation on two European populations

Classification performance of the Sella-Tunis et al. (2017) sex estimation method in Czech population: different posterior probability threshold approaches

In this study we tested classification performance of a sex estimation method from the mandible originally developed by Sella-Tunis et al. (2017) on a heterogeneous Israeli population. Mandibular linear dimensions were measured on 60 CT scans derived from the Czech living population. Classification performance of Israeli discriminant functions (DFs-IL) was analyzed in comparison with calculated Czech discriminant functions (DFs-CZ) while different posterior probability thresholds (currently discussed in the forensic literature) were employed. Our results comprehensively illustrate sensitivity of different discriminant functions to population differences in body size and degree of sexual dimorphism. We demonstrate that the error rate may be biased when presented per posterior probability threshold. DF-IL 1 showed least sensitivity to population origin and fulfilled criteria of sufficient classification performance when applied on the Czech sample with a minimum posterior probability threshold of 0.88 reaching overall accuracy ≥ 95%, zero sex bias, and 80% of classified individuals. The last parameter was higher in DF-CZ 1 which was the main difference between those two DFs suggesting relatively low dependance on population origin. As the use of population-specific methods is often prevented by complicated assessment of population origin, DF-IL 1 is a candidate for a sufficiently robust method that could be reliably applied outside the reference sample, and thus, its classification performance deserves further testing on more population samples.

Sexual dimorphism of the twelfth thoracic vertebra for sex determination in the Central Thai population

Analyzing skeletal remains is crucial for identifying individuals, and forensic anthropologists use this analysis to determine biological characteristics, particularly sex, aiding criminal investigations. Among thoracic vertebrae, the twelfth thoracic vertebra (T12) is highly sexually dimorphic in various populations. This study aims to establish a discriminant function equation (DFE) for sex determination based on T12 in the Central Thai population. A total of 15 parameters of T12 were examined in 69 bone samples (43 males and 26 females). Among the 15 parameters, 14 were significantly different between males and females. The discriminant function equation (DFE) was generated as DFE = -19.578 + 0.376(i) BDsm + 0.254(l) PW + 0.081TDm, with a cutoff value of -0.296 for males and females, showing 92.8 % accuracy. The evaluation of the DFE using 10 blind samples showed 90 % accuracy. These findings may offer an additional method for sex determination through T12, complementing the examination of other skeletal elements.

Machine Learning Models for Prediction of Sex Based on Lumbar Vertebral Morphometry

BACKGROUND

Identifying skeletal remains has been and will remain a challenge for forensic experts and forensic anthropologists, especially in disasters with multiple victims or skeletal remains in an advanced stage of decomposition. This study examined the performance of two machine learning (ML) algorithms in predicting the person's sex based only on the morphometry of L1-L5 lumbar vertebrae collected recently from Romanian individuals. The purpose of the present study was to assess whether by using the machine learning (ML) techniques one can obtain a reliable prediction of sex in forensic identification based only on the parameters obtained from the metric analysis of the lumbar spine.

METHOD

This paper built and tuned predictive models with two of the most popular techniques for classification, RF (random forest) and XGB (xgboost). Both series of models used cross-validation and a grid search to find the best combination of hyper-parameters. The best models were selected based on the ROC_AUC (area under curve) metric.

RESULTS

The L1-L5 lumbar vertebrae exhibit sexual dimorphism and can be used as predictors in sex prediction. Out of the eight significant predictors for sex, six were found to be particularly important for the RF model, while only three were determined to be important by the XGB model.

CONCLUSIONS

Even if the data set was small (149 observations), both RF and XGB techniques reliably predicted a person's sex based only on the L1-L5 measurements. This can prove valuable, especially when only skeletal remains are available. With minor adjustments, the presented ML setup can be transformed into an interactive web service, freely accessible to forensic anthropologists, in which, after entering the L1-L5 measurements of a body/cadaver, they can predict the person's sex.

Deep learning in sex estimation from a peripheral quantitative computed tomography scan of the fourth lumbar vertebra-a proof-of-concept study

Sex estimation is a key element in the analysis of unknown skeletal remains. The vertebrae display clear sex discrepancy and have proven accurate in conventional morphometric sex estimation. This proof-of-concept study aimed to investigate the possibility to develop a deep learning algorithm for sex estimation even from a single peripheral quantitative computed tomography (pQCT) slice of the fourth lumbar vertebra (L4). The study utilized a total of 117 vertebrae from the Terry Anatomical Collection. There were 58 male and 59 female cadavers, all of the white ethnicity, with the average age at death 49 years and a range of 24 to 77 years. A coronal pQCT scan was taken from the midway of the L4 corpus. Sex estimation was performed in a total of 19 neural network architectures implemented in the AIDeveloper software. Of the explored architectures, a LeNet5-based algorithm reached the highest accuracy of 86.4% in the test set. Sex-specific classification rates were 90.9% among males and 81.8% among females. This preliminary finding advances the field by encouraging and directing future research on artificial intelligence-based methods in sex estimation from individual skeletal traits such as the vertebrae. Combining quickly obtained imaging data with automated deep learning algorithms may establish a valuable pipeline for forensic anthropology and provide aid when combined with traditional methods.

Deep learning and morphometric approach for Sex determination of the lumbar vertebrae in a Thai population

Sex determination is a fundamental step in biological profile estimation from skeletal remains in forensic anthropology. This study proposes deep learning and morphometric technique to perform sex determination from lumbar vertebrae in a Thai population. A total of 1100 lumbar vertebrae (L1-L5) from 220 Thai individuals (110 males and 110 females) were obtained from the Forensic Osteology Research Center, Faculty of Medicine, Chiang Mai University, Thailand. In addition, two linear measurements of superior and inferior endplates from the digital caliper and image analysis were carried out for morphometric technique. Deep learning applied image classification to the superior and inferior endplates of the lumbar vertebral body. All lumbar vertebrae images are included in the dataset to increase the number of images per class. The accuracy determined the performance of each technique. The results showed the accuracies of 82.7%, 90.0%, and 92.5% for digital caliper, image analysis, and deep learning techniques, respectively. The lumbar vertebrae L1-L5 exhibit sexual dimorphism and can be used in sex estimation. Deep learning is more accurate in determining sex than the morphometric method. In addition, the subjectivity and errors in the measurement are decreased. Finally, this study presented an alternative approach to determining sex from lumbar vertebrae when the more traditionally used skeletal elements are incomplete or absent.

Machine learning and discriminant function analysis in the formulation of generic models for sex prediction using patella measurements

Sex prediction from bone measurements that display sexual dimorphism is one of the most important aspects of forensic anthropology. Some bones like the skull and pelvis display distinct morphological traits that are based on shape. These morphological traits which are sexually dimorphic across different population groups have been shown to provide an acceptably high degree of accuracy in the prediction of sex. A sample of 100 patella of Mixed Ancestry South Africans (MASA) was collected from the Dart collection. Six parameters: maximum height (maxh), maximum breadth (maxw), maximum thickness (maxt), the height of articular facet (haf), lateral articular facet breadth (lafb), and medial articular facet breath (mafb) were used in this study. Stepwise and direct discriminant function analyses were performed for measurements that exhibited significant differences between male and female mean measurements, and the "leave-one-out" approach was used for validation. Moreover, we have used eight classical machine learning techniques along with feature ranking techniques to identify the best feature combinations for sex prediction. A stacking machine learning technique was trained and validated to classify the sex of the subject. Here, we have used the top performing three ML classifiers as base learners and the predictions of these models were used as inputs to different machine learning classifiers as meta learners to make the final decision. The measurements of the patella of South Africans are sexually dimorphic and this observation is consistent with previous studies on the patella of different countries. The range of average accuracies obtained for pooled multivariate discriminant function equations is 81.9-84.2%, while the stacking ML technique provides 90.8% accuracy which compares well with those presented for previous studies in other parts of the world. In conclusion, the models proposed in this study from measurements of the patella of different population groups in South Africa are useful resent with reasonably high average accuracies.

The impact of age on the morphology of the 12th thoracic vertebral endplates

The current article explores the aging effects on the overall morphology of the endplates of the 12th thoracic vertebra (T12), while screening for sex differences. It further evaluates the suitability of T12 for estimating age-at-death in bioarcheaological contexts. We captured the morphology of the vertebral endplates, including the formation of osteophytes, in a novel continuous quantitative manner using digital photography. 168 Greek adults from the Athens Collection were used for modeling the aging effects and another 107 individuals from two Danish archaeological assemblages for evaluation. Regression analysis is based on generalized additive models for correlating age-at-death and morphological variation. Our proposed measurement method is highly reliable (R>0.98) and the main differences observed between sexes are size related. Aging has considerable effect on the endplate morphology of the T12 with the total area of the endplate, the area of the epiphyseal rim, and the shape irregularities of the endplate's external boundary being mostly affected. Multivariate regression shows that aging effects account up to 46% of the observed variation, although with differential expression between sexes. Correct age prediction on archaeological remains reached 33% with a prominent tendency for overestimation. The morphology of the T12 endplates is influenced by age and it can provide some insight with respect to the age-at-death of unidentified individuals, especially when other skeletal age markers are unavailable. Our proposed method provides an age-estimation framework for bioarchaeological settings, especially for estimating broader age ranges, such as discriminating between young and old adults.

Forensic Anthropology and Archaeology in Denmark

In this paper, we provide a brief overview of the status of forensic anthropology and forensic archeology in Denmark, as well as related information about education, research, and skeletal collections. Forensic anthropologists mainly deal with the examination of unidentified skeletal remains. Some special tasks include cranial trauma analysis of the recently deceased, advanced 3D visualization from CT scanning of homicide cases, and stature estimation of perpetrators using surveillance videos. Forensic anthropologists are employed at one of Denmark’s three departments of forensic medicine (in Copenhagen, Odense, and Aarhus) and have access to advanced imaging equipment (e.g., CT and MR scanning, surface scanners, and 3D printers) for use in both their requisitioned work and their research. Extensive research is conducted on different topics, such as the health and diseases of past populations, age estimation, and human morphology. Research is based on skeletal material from the archeological collections housed in Copenhagen and Odense or on CT data from the recently deceased. There is no full degree in forensic anthropology in Denmark, but elective courses and lectures are offered to students at different levels and to people from different professional backgrounds.

Forensic archaeology is a relatively new field of expertise in Denmark, and relevant cases are rare, with only one or two cases per year. No forensic archeologists are officially employed in any of the departments of forensic medicine. Until recently, the Special Crime Unit of the police handled crime scene investigations involving excavations, but with the option of enlisting the help of outside specialists, such as archaeologists, anthropologists, and pathologists. An official excavation work group was established in 2015 under the lead of the Special Crime Unit of the police with the aim of refining the methods and procedures used in relevant criminal investigations. The group is represented by five police officers from the Special Crime Scene Unit, a police officer from the National Police Dog Training center, the two archaeologists from Moesgaard Museum, a forensic anthropologist from the Department of Forensic Medicine (University of Copenhagen), and a forensic pathologist from the Department of Forensic Medicine (University of Aarhus).

Sex estimation using the human vertebra: a systematic review

Background

The vertebral column has been used in forensic studies for its weight-bearing function and relative density. Sex estimation is one of the essential elements in an anthropological examination, as it may narrow down the possibility of a match by half. Hence, it is crucial to derive the population-specific reference data in each vertebra for sex estimation. This systematic review explored the most sexually dimorphic vertebra by using the conventional anthropometric analysis.

Main body

An electronic comprehensive search was conducted using databases such as Scopus, Web of Science (WOS) and EBSCO Medline for relevant studies between 2008 and 2020. The main inclusion criteria were studies in English, and studies on sex estimation by morphometric analysis of vertebra by CT scan or dry bone. Only studies related to human adult age and vertebra were analysed. Literature search identified 84 potentially relevant articles, in which 19 articles had fulfilled the inclusion criteria. This review included studies on the cervical, thoracic and lumbar vertebrae in different populations.

Conclusion

The vertebral spine has demonstrated significant sexual dimorphism with variable prediction accuracies, whereby the body of a vertebra was found to be sexually dimorphic. It was shown that high accuracy of sex classification was provided by the second cervical, twelfth thoracic and first lumbar vertebrae, especially when they were used in combination.