The review’s central finding is that thermal exposure produces a recognizable progression of macroscopic and microscopic changes. As heat intensifies, bone shifts in color from brownish and blackened hues toward gray, then chalky white as organic components burn away and the mineral phase calcines. Alongside this chromatic journey come shrinkage, warping, and a characteristic suite of fractures. These changes are not governed by peak temperature alone. The authors emphasize that oxygen availability, the duration of combustion, and the overall burning conditions all shape the final appearance of the remains, which helps explain why two bodies exposed to the same nominal fire can emerge with markedly different skeletal damage.

That multi-factorial picture matters because forensic anthropologists routinely try to reconstruct fire circumstances from burned remains. Color, for instance, has long been treated as a rough thermometer, and the review highlights work using spectrophotometric and colourimetric analysis to make that assessment more objective. Studies cited in the review measured bone color quantitatively to estimate the temperature of exposure in cortical and medullary bone, while other research examined how prolonged heating affects both color and crystallinity. The mineral crystals of bone grow and reorganize as heat destroys the collagen scaffold, and techniques such as FTIR-ATR spectroscopy and X-ray diffraction can track those crystal-size changes, offering a chemical complement to visual inspection.

The microscopic scale tells an equally important story. Histological studies of heated compact bone, including work on human rib tissue, show that the microstructure of bone degrades in ways that can interfere with established methods of age estimation, which often rely on examining bone remodeling features under the microscope. The review notes that research has tested whether histological age estimation remains viable in burned ribs, and that the survival of age-at-death diagnostic features under heat is a genuine concern. One cited study found that heat-induced changes affect the skeletal features used for age-at-death estimation, underscoring that fire does not merely damage bone superficially but can erase or distort the very markers anthropologists depend upon.

Warped and shrunken bone poses a parallel problem for the biological profile, the set of estimates covering age, sex, ancestry, and stature that anchors identification efforts. The review draws on experimental work showing that heat-induced dimensional changes in bone have direct consequences for metric analysis, since measurements taken after burning no longer match the pre-burning skeleton. Yet the news is not uniformly grim. Studies on osteometric sex determination of burned remains and performance tests of stature estimation methods in burned skeletal material, including a Portuguese population sample, suggest that metric approaches can still perform usefully when their limitations are understood. Research into the pre-burning condition of remains aims to correct for shrinkage and distortion, effectively reconstructing what the skeleton looked like before the fire.

Perhaps the most forensically charged question the review addresses is trauma analysis: can investigators still distinguish a perimortem fracture, inflicted around the time of death, from a fracture caused by the fire itself? The literature assembled here says it is sometimes possible, though never straightforward. Classic work on differentiating traumatic from heat-related fractures established diagnostic criteria, and more recent studies have tested the differentiation of mechanical versus thermal damage as causes of bone fractures. Researchers have examined whether cranial perimortem fractures remain identifiable in cremated remains, whether perimortem traits survive on burnt long bones, and even whether dismemberment-related toolmarks and pre-existing ballistic trauma can be detected after burning. The review’s verdict is that trauma examination remains feasible in some cases, but fragmentation and calcination may obscure the diagnostic features on which such determinations depend.

Underlying all of these applications is the basic material science of bone as a composite. Bone combines a flexible organic phase, dominated by collagen, with a stiff mineral phase of hydroxyapatite. Heat attacks both. Vibrational spectroscopy studies cited in the review explored how the collagen content of human bone influences heat-induced warping, capturing the idea that bone with more surviving collagen tends to deform by yielding rather than shattering. As temperatures rise, water and organic matter are driven off, the mineral recrystallizes, and the bone becomes brittle and calcined. Scanning electron microscope observations of incinerated human femoral bone documented these microstructural transformations in detail, and comparative work on burning under oxidizing versus reducing conditions showed that the chemical atmosphere of the fire itself leaves detectable signatures in the bone.

The review also situates burned-bone analysis within the broader operational world of fatal fire investigation. Forensic anthropologists are increasingly involved in the search and recovery of victims from structure fires and wildland fires, and in disaster victim identification, where burned and fragmented remains are common. Studies on the degree of bodily destruction relative to the duration of the fire, and postmortem computed tomography findings of thermal bone injuries in actual cases, illustrate how anthropological interpretation now intersects with imaging and fire-science expertise. Classification systems that categorize thermal damage by body segment aim to standardize how investigators document and communicate burn patterns, and experimental work on accidental versus deliberate burning scenarios explores whether bone can reveal whether a fire was intentional.

What emerges from the systematic review is a discipline maturing toward methodological self-awareness. The authors conclude that the effects of thermal alteration vary according to the component of the biological profile being assessed, the methods employed, and the degree of skeletal preservation, a formulation that effectively warns against one-size-fits-all interpretation of burned remains. Understanding the mechanisms of heat-induced skeletal change, they argue, improves the examination of burned human remains and supports more reliable forensic anthropological analysis in fire-related investigations. For a field whose conclusions can shape criminal prosecutions and identification of fire victims, that mechanistic grounding is not academic luxury. It is the difference between reading the skeleton’s fiery testimony accurately and mistaking the fire’s own handiwork for evidence of violence.

Subject of Research: Heat-induced structural and chemical alterations of human skeletal remains and their implications for forensic anthropological analysis

Article Title: Thermal alteration of skeletal remains in forensic anthropology: a systematic review

Article References: Cevahiroglu, N., & Buyuksungur, A. (2026). Thermal alteration of skeletal remains in forensic anthropology: a systematic review. International Journal of Legal Medicine. https://doi.org/10.1007/s00414-026-03995-8

Image Credits: AI Generated

DOI: 10.1007/s00414-026-03995-8

Keywords: forensic anthropology, burned bone, thermal alteration, calcination, biological profile, trauma analysis, heat-induced fractures, bone color, systematic review, fire investigation, age estimation, shrinkage

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 research, published in the International Journal of Legal Medicine, was led by Suguru Torimitsu of the University of Tokyo and Chiba University, together with collaborators including Eiryo Kawakami and Yohsuke Makino. The team set out to test whether a so-called foundation model—a large, pretrained neural network of the kind that has transformed medical imaging—could simultaneously estimate both sex and age from three-dimensional computed tomographic images of the femur. Their cohort was drawn from postmortem CT scans of 1,489 Japanese individuals, 1,003 males and 486 females, aged between 20 and 90 years, all of whom underwent whole-body PMCT followed by forensic autopsy at the two universities between May 2015 and November 2025.

The technical pipeline behind the study is a showcase of modern deep learning practice. First, the femora were automatically segmented from whole-body scans using the TotalSegmentator library, which isolates anatomical structures without manual tracing. Each segmented femur was then converted into a three-dimensional volume of 112 by 112 by 112 voxels, with voxels outside the bone mask replaced by air-equivalent values to suppress background noise, and intensity normalized using percentile-based scaling. Rather than feeding the model selected two-dimensional slices, the researchers processed the entire volumetric bone, allowing the network to learn from the complete three-dimensional morphology of the femur.

At the heart of the system sits 3DINO, a three-dimensional Vision Transformer foundation model pretrained on large-scale medical imaging data. Vision Transformers work by dividing an image into patches and encoding the relationships between those patches through attention mechanisms, which let the model weigh the importance of different regions dynamically. To adapt this general-purpose backbone to the forensic task, the team used low-rank adaptation, or LoRA, a parameter-efficient fine-tuning technique in which only small, low-rank matrices inserted into the network are updated during training while the original pretrained weights remain frozen. This approach dramatically reduces the number of trainable parameters, lowering the risk of overfitting on a dataset of modest size and shortening training time.

The framework was trained in a multitask learning configuration, meaning a single shared feature-extraction backbone fed two separate output heads: one performing binary classification for sex, and one performing regression for age, with predicted values scaled to the physiological range through a sigmoid function. The total loss was a weighted sum of the classification and regression losses, enabling joint optimization. The intuition is biologically sound: sex-related morphology and age-related structural change are correlated aspects of the same skeleton, so learning them together can produce richer internal representations than learning either in isolation. Training used the AdamW optimizer with a cosine-annealing learning-rate schedule, data augmentation including random cropping and affine transformations, and early stopping to prevent overfitting. The dataset was split into a training-and-validation cohort and an independent test cohort in a four-to-one ratio, with stratified sampling by age.

The results, evaluated on the independent test set, were striking. For sex estimation, the model achieved balanced accuracies of 0.897 for the left femur and 0.929 for the right femur, with area under the receiver operating characteristic curve values of 0.979 and 0.988 respectively—figures the authors describe as favorable compared with previous reports. Earlier CT-based morphometric studies in Japanese populations had produced accuracies of roughly 73 percent from femoral length alone, rising to around 88 to 90 percent when combining measurements such as femoral epicondylar breadth and head diameter. The deep learning approach, which captures complex nonlinear morphological patterns beyond what linear measurements can express, matched or surpassed those benchmarks without any manual feature extraction. Calibration metrics, including Brier scores and expected calibration error, were both below 0.1, indicating that the model’s predicted probabilities were trustworthy rather than merely accurate on average.

Age estimation proved harder, as it always does in forensic anthropology, where adult skeletal indicators are notoriously imprecise. The models achieved a mean absolute error of approximately eight years and a root mean square error of approximately ten years. Notably, performance was better for female individuals than for males, with lower error values and a higher coefficient of determination in the female subgroup. Bland–Altman analysis revealed mean biases close to zero, but also a familiar statistical signature: the model tended to overestimate the ages of younger individuals and underestimate those of older ones, a regression toward the mean that plagues most skeletal aging methods. The authors suggest that age-related morphological variation in the femur may be more consistent in females, while greater interindividual variability in males reduces accuracy; they also note that postmenopausal estrogen deficiency may shape female femoral morphology in ways the model can exploit.

Perhaps the most scientifically intriguing findings came from the attention maps, which reveal where in the bone the network was looking when it made its decisions. For sex estimation, attention concentrated overwhelmingly in the distal femur—the lower end near the knee—regardless of age group, a result consistent with decades of osteometric research showing that the epicondylar breadth of the distal femur is among the most sexually dimorphic measurements available. For age estimation, however, attention shifted to the femoral shaft, the long diaphyseal midsection, which in the right femur accounted for the highest attention in nearly all cases across both age groups. This dissociation suggests the model is genuinely learning distinct biological signals rather than relying on a single anatomical shortcut, and it points forensic researchers toward diaphyseal features as an underexplored source of age-related information.

One unexpected observation was a laterality effect: the right femur model consistently outperformed the left, and attention maps from the left femur showed greater variability in the contributing regions, particularly among older individuals. The authors caution that the reasons remain unclear and that further work is needed to determine whether this reflects biology, imaging conditions, or methodological artifacts. They are equally candid about other limitations. The study included only intact femora from Japanese individuals, so it remains unknown whether the models work on fragmented or decomposed remains—precisely the conditions most common in real forensic casework. Skeletal sexual dimorphism also varies across populations, meaning standards derived from Japanese data may not transfer directly elsewhere, and factors such as nutrition, lifestyle, occupation, and cultural habits were not examined.

Even with those caveats, the implications are considerable. Automated segmentation and foundation-model feature extraction remove the reliance on manual measurements, improving reproducibility and objectivity while enabling rapid analysis of large datasets—capabilities that are especially valuable in time-sensitive scenarios such as disaster victim identification, where hundreds of remains may need assessment under acute pressure. The multitask design means a single scan of a single bone yields both components of the biological profile at once. The authors argue that extending AI-based approaches to understudied skeletal elements could advance the field further, and they call for larger, more heterogeneous datasets, including multi-population cohorts and incomplete skeletal material, to validate and generalize the approach. If those efforts succeed, the humble thigh bone—already the bone most likely to survive environmental degradation—may become the backbone of a new generation of automated forensic identification, in which an algorithm reads the story written in bone faster, and perhaps more consistently, than any human expert.

Subject of Research: Deep learning–based estimation of age and sex from femoral postmortem CT images in a Japanese population

Article Title: Age and sex estimation from Japanese femoral computed tomographic images using foundation models

Article References: Torimitsu, S., Ikari, H., Tsuneya, S., Uemura, Y., Okada, M., Kojima, M., Al Mansoori, R., Iwase, H., Kawakami, E., & Makino, Y. (2026). Age and sex estimation from Japanese femoral computed tomographic images using foundation models. International Journal of Legal Medicine. https://doi.org/10.1007/s00414-026-03998-5

Image Credits: AI Generated

DOI: 10.1007/s00414-026-03998-5

Keywords: forensic anthropology, deep learning, foundation models, postmortem CT, femur, sex estimation, age estimation, Vision Transformer, LoRA, multitask learning, biological profile, victim identification