The research team, led by Thandolwethu Mbali Mbonani of the University of Pretoria, tackled one of the most stubborn problems in forensic anthropology. When unidentified skeletal remains are found, investigators build a biological profile covering age, sex, stature and population affinity. In South Africa, that last component is complicated by a demographic landscape shaped by migration, admixture and apartheid-era classification. Official statistics recognise four broad categories, Black African, Coloured, Indian/Asian and White, which are socially and historically constructed rather than discrete biological units, yet they remain embedded in missing-person reports and medicolegal documentation. Estimating affinity therefore means comparing an unknown individual probabilistically against documented reference samples, not assigning a fixed biological label.

Existing reference data, largely drawn from skeletal collections assembled over decades, do not fully reflect the complexity of the contemporary population. The new study sidestepped that limitation by turning to living people. The researchers analysed 474 retrospective cranial computed tomography scans acquired between 2017 and 2019 at three tertiary academic hospitals: Groote Schuur Hospital, Inkosi Albert Luthuli Central Hospital and Steve Biko Academic Hospital. The scans represented recorded Black, Coloured, Indian and White South Africans, with ages ranging from 18 to 89 and a mean of 46 years. Scans showing major craniofacial trauma, congenital anomalies or previous surgery were excluded, and all data were anonymised under ethical approval from the University of Pretoria.

The technical heart of the study is a template-based automatic landmarking workflow. Rather than placing anatomical landmarks by hand on each of hundreds of skull models, a laborious and observer-dependent process, the team used a previously developed reference cranial template of 63,772 vertices. Six landmarks, including rhinion, nasospinale, the alare points and the porion points, were placed manually to initialise alignment. The template was then rigidly aligned and warped onto each individual cranial surface using progressively more flexible non-rigid registration, establishing point-wise anatomical correspondence. Eighteen three-dimensional landmarks were thereby transferred automatically to every skull, yielding 54 coordinate variables per person and, from those coordinates, nine inter-landmark distances spanning cranial length, frontal and orbital breadth, nasal dimensions, facial breadth and mastoid height.

Reliability testing showed the automation holds up under scrutiny. Mean intra-observer landmark error was 0.746 millimetres and mean inter-observer error 1.672 millimetres, with most landmarks falling within a practical 2-millimetre benchmark. A handful of landmarks on broad, curved or poorly defined regions, notably opisthocranion and glabella, showed higher inter-observer variability, but the derived linear measurements were highly precise, with relative technical errors well below published anthropometric guidelines. The results confirm that automatically transferred landmarks are suitable for population-level analysis, though the authors caution that expert decisions during template construction and initial placement still matter.

With the landmark data in hand, the researchers compared two representations of cranial form. Geometric morphometrics preserves the full spatial configuration of landmarks after mathematically standardising for position, orientation and scale through generalised Procrustes analysis, capturing coordinated patterns of shape variation across the whole skull. Inter-landmark distances, by contrast, reduce the configuration to selected pairwise linear measurements that are easy to interpret in conventional craniometric terms but discard much of the geometric information. Shape differences among the four recorded groups were statistically significant in every pairwise comparison, with the greatest separation between Black and White South Africans and the greatest overlap between Coloured and Indian South Africans.

The classification results were striking. Cross-validated discriminant function analysis built on the geometric morphometric shape variables achieved an overall accuracy of 90.08 percent, highest for Black South Africans at 94.78 percent and lowest for Coloured South Africans at 82.35 percent. The linear-measurement models lagged far behind: linear discriminant analysis reached 58.7 percent and random forest classification 52.0 percent in cross-validation, with independent validation accuracies of 53.8 and 56.4 percent respectively. Random forest variable-importance analysis ranked nasal breadth as the single most informative predictor, followed by minimum frontal breadth and right mastoid height, echoing earlier South African research that has repeatedly flagged nasal and midfacial morphology as highly differentiated.

The authors are careful not to overinterpret the gap. Their comparison pitted a complete 18-landmark configuration against only nine selected distances, so the result shows that this particular shape configuration retained more classification-relevant information than this particular measurement set, not that linear measurements are inherently inferior. Previous work, including a well-known comparison by Spradley and Jantz in North American samples, found the opposite pattern with non-standard inter-landmark distances. Classification success, the study concludes, depends on the reference population, the landmark configuration, the measurements chosen and the analytical framework, rather than on any universally superior method.

The overlap between Coloured and Indian South Africans is not a methodological failure but a reflection of history and biology. Coloured South Africans descend from admixed populations with contributions from Indigenous southern African, sub-Saharan African, European, South Asian and Southeast Asian sources, while Indian South Africans represent heterogeneous migration histories from different regions of the subcontinent. Statistically detectable group-level trends coexist with continuous variation, and the study reinforces that even 90 percent group-level accuracy should never be read as evidence that recorded categories correspond to discrete biological entities. Misclassifications clustered exactly where that continuity predicts, with Coloured individuals most often misassigned as White and considerable confusion between Coloured and Indian groups.

For forensic practice, the implications are twofold. Shape-based classification could in principle be applied to an unknown cranium by generating a three-dimensional model, transferring landmarks with the validated protocol and projecting the configuration into the reference morphospace, but routine adoption faces real barriers: the cost and availability of CT scanning for unidentified remains, and the specialised software, infrastructure and training required. Whatever the method, the authors argue, results must be reported probabilistically, considering posterior probabilities and the degree of overlap among the most likely groups, and integrated with DNA, dental evidence and missing-person records. Where probabilities are low or similar across groups, an ambiguous result should be reported as such rather than forced into the numerically highest category. External validation on independent contemporary samples, and evaluation on fragmented remains, remain necessary before the approach enters routine casework, but the study marks a significant step toward reference data and methods that reflect the populations forensic scientists actually serve.

Subject of Research: Population affinity estimation from cranial CT scans using geometric morphometrics and automatic landmarking in South African forensic anthropology

Article Title: Evaluating classification approaches for population affinity estimation in a contemporary South African CT-derived sample: an automatic landmarking-based approach

Article References: Mbonani, T. M., L’Abbé, E. N., Chen, D.-G., Krüger, G. C., & Ridel, A. F. (2026). Evaluating classification approaches for population affinity estimation in a contemporary South African CT-derived sample: an automatic landmarking-based approach. International Journal of Legal Medicine. https://doi.org/10.1007/s00414-026-03997-6

Image Credits: AI Generated

DOI: 10.1007/s00414-026-03997-6

Keywords: forensic anthropology, population affinity, geometric morphometrics, computed tomography, automatic landmarking, South Africa, cranial shape, inter-landmark distances, discriminant function analysis, random forest, human variation, biological profile

The cranium has always been the workhorse of ancestry estimation in forensic anthropology. In adults, standard craniometrics, the measurement of linear distances between anatomical landmarks on the skull, remains one of the most widely used, repeatable and cost-effective methods for estimating population affinity, with South African adult studies achieving classification accuracies of roughly seventy to eighty-four percent. The midface and cranial base are particularly informative because they exhibit population-specific variation while also surviving intact in many forensic cases. Yet translating these adult tools to children has proven remarkably difficult. Documented subadult skeletal collections are scarce, and the cranium undergoes dramatic growth and remodelling throughout childhood, with the neurocranium, midface and cranial base each maturing on distinct developmental timetables. Growth, many researchers assumed, simply swamps the population signal before it can be measured.

The new study tested that assumption directly. The researchers obtained anonymised CBCT scans from four South African hospitals: Groote Schuur Hospital in Cape Town, Cintocare private hospital in Pretoria, Victoria Mxenge Hospital in Durban and Charlotte Maxeke Johannesburg Academic Hospital. The sample comprised ninety-four black South Africans, fifty-one coloured South Africans, twenty-six white South Africans and ten Indian South Africans, all imaged at resolutions between 0.25 and 0.40 millimetres. Children with facial deformities from reconstructive surgery, pathology or trauma were excluded. Using the Avizo software platform, the team aligned each scan to the Frankfurt horizontal plane and segmented the three-dimensional models to isolate bone from soft tissue, then placed up to thirty-two cranial landmarks on each cranium, from which twenty-two standard linear measurements, called interlandmark distances, were automatically calculated.

Reliability came first. The researchers calculated technical error of measurement for both repeated measurements by the same observer and independent measurements by a second observer. Intra-observer error averaged just 0.38 millimetres, well within accepted anthropological thresholds, while inter-observer error averaged 0.68 millimetres. These results confirm that cranial dimensions can be collected accurately and repeatably from three-dimensional virtual models of children, echoing earlier work by Corron and colleagues on virtual subadult crania. With the measurement framework validated, the team turned to the central question: do these numbers actually differ between population groups, and can a statistical model exploit those differences?

The answer, cautiously, is yes. Non-parametric Kruskal-Wallis tests revealed statistically significant population differences in ten of the twenty-two measurements, concentrated in the orbital, nasal and basicranial regions. Black South Africans generally showed the largest orbital and midfacial dimensions, white South Africans the greatest upper facial and nasal height measurements, coloured South Africans greater midface and cranial base dimensions, and Indian South Africans smaller overall cranial size but larger nasal projection and malar height. Black and white South Africans overlapped the least, while Indian and coloured South Africans showed substantial morphological overlap, a pattern consistent with the country’s complex history of admixture, colonial-era migration, slavery and apartheid-era population classification, which shaped both gene flow and the demographic structure of modern South African groups.

For classification, the team employed linear discriminant analysis, a statistical method long used in biological anthropology, refined with backward-stepwise variable selection and ten-fold cross-validation to guard against overfitting. The best-performing model, which combined facial and cranial base measurements, achieved an overall classification accuracy of 79.17 percent with a kappa statistic of 0.67, and a closely related combined model reached 81.94 percent in testing. Black South Africans were classified most accurately, exceeding eighty-seven percent in the combined model, followed by white South Africans at nearly eighty-one percent and coloured South Africans at roughly seventy percent. Indian South Africans fared worst, correctly classified only half the time, a shortfall the authors attribute to the very small sample of ten individuals and to genuine biological heterogeneity within a population descended largely from indentured labourers brought to KwaZulu-Natal under British rule.

Perhaps the most intriguing result concerns age. When the models were stratified by developmental stage, classification accuracy dropped: juveniles aged seven to twelve were classified at 62.50 percent and adolescents aged thirteen to sixteen at 64.52 percent, both below the performance of the age-pooled model. Counterintuitively, pooling the full developmental sample produced better results than splitting it, with the pooled model achieving 88.89 percent accuracy in the three-to-six-year cohort, 84.09 percent at seven to twelve and 75.82 percent at thirteen to sixteen. The authors suggest that early childhood represents a period of morphological convergence in which growth trajectories across populations align and obscure population-specific distinctions, while the pooled model benefits from capturing the broader spectrum of cranial variation across development. In practical terms, this means forensic practitioners may not even need to know a child’s age before attempting an ancestry estimate from cranial measurements.

The comparison with adults adds an evolutionary twist. The subadult model actually outperformed a comparable adult South African classification model, which managed only 62.20 percent accuracy. The researchers propose that in children, where sexual dimorphism is minimal and adult shape remodelling has not yet occurred, overall cranial size carries a cleaner population signal. As individuals mature, divergent male and female growth trajectories, vault expansion and facial projection progressively mask these size-driven differences, shifting the population signature from size to shape. This may explain why orbital measurements showed significant population differences in the subadult sample but not in adult data, and why earlier work by the same team found that macromorphoscopic traits, which capture more complex shape information, performed poorly in South African subadults.

The study is careful to emphasise its limitations. Sample sizes were unequal across population groups and age cohorts, and small samples, particularly for Indian South Africans, raise the risk of overfitting and optimistic accuracy estimates. Substantial cranial overlap persisted across all groups, and the authors stress that population affinity estimates must always be framed probabilistically rather than as definitive attributions, especially in a country whose population history makes morphology a nuanced and sometimes ambiguous witness. Nonetheless, the work delivers three concrete contributions: it demonstrates that population-specific variation is detectable in the craniofacial size of South African children, particularly in the midface and cranial base; it provides the first baseline classification models for South African subadults, identifying size-based craniometrics as the most promising avenue for refinement; and it underscores that knowledge of population history and genetics is indispensable when building identification tools for the youngest and most vulnerable victims. As virtual anthropology, hospital imaging archives and machine learning continue to expand the forensic toolkit, this study signals that the skulls of children, far from being silent, have been carrying identifiable stories all along, and that science is finally learning to read them.

Subject of Research: Craniometric estimation of population affinity from CBCT-derived three-dimensional cranial models of South African subadults

Article Title: Craniometric evaluation of population affinity among South African subadults

Article References: Harripershad, M., Stull, K. E., Ridel, A. F., Theye, C. E. G., & Liebenberg, L. (2026). Craniometric evaluation of population affinity among South African subadults. International Journal of Legal Medicine. https://doi.org/10.1007/s00414-026-04018-2

Image Credits: AI Generated

DOI: 10.1007/s00414-026-04018-2

Keywords: forensic anthropology, population affinity, craniometrics, CBCT, subadults, interlandmark distance, linear discriminant analysis, cranial base, midface, South Africa, Craniometric, evaluation