Kyra E. Stull

Clinical computed tomography (CT) datasets are increasingly common in skeletal research, yet archived retrospective datasets with thick-slice, anisotropic reconstructions are more commonly available to researchers than the original high-resolution scans. Models reconstructed from these suboptimal scans can produce distorted measurements and irregular surfaces. This study evaluates a super-resolution reconstruction (SRR) framework for generating skeletal models with improved surface smoothness and continuity from multiple orthogonal, thick-slice CT stacks. Archived CT scans of long bones from 30 individuals (0–16 years) were collected from National Taiwan University Hospital. For each individual, 3D models were generated from both the original thick-slice stacks and SRR-processed volumes. Linear measurements were compared with those taken directly from high-resolution picture archiving and communication system (PACS) renderings to assess metric consistency. Geometric similarity between original thick-slice models and SRR-reconstructed models was quantified by signed surface deviation and Dice similarity coefficient. Because both model types were derived from the same image stacks, this geometric comparison further quantifies consistency between reconstruction approaches. SRR-reconstructed models showed lower measurement error and greater agreement with the PACS-derived measurements than the thick-slice models. Surface geometry was generally consistent across model types, with localized deviations concentrated at metaphyseal regions. SRR processing also produced smoother surfaces with a clearer separation of fusing elements. These results demonstrate that SRR is a practical tool to improve surface continuity in virtual skeletal models derived from suboptimal clinical imaging.

Although standard craniometrics is typically regarded as the method of choice for estimating population affinity in adults, its application to subadults has received limited attention, despite potential population-specific signals demonstrated in cranial growth patterns. This study evaluated the potential efficacy of interlandmark distances (ILDs) derived from three-dimensional (3D) cranial models obtained from Cone Beam Computed Tomography (CBCT) scans of South African black, coloured, indian and white subadults, and linear discriminant analysis (LDA) to estimate population affinity. Results indicated that the combined midface and cranial base model contained the most population-specific variation, with black South Africans achieving the highest classification accuracy. The best-performing multivariate model achieved a 79.17% accuracy and a kappa of 0.67. Age-stratified models produced slightly lower classification rates, suggesting that early ontogenetic trajectories during childhood may obscure population differences. Indian South Africans were consistently misclassified across all models, likely reflecting both challenges posed by limited sample size and substantial heterogeneity and overlap with other groups. While age had a limited influence on the predictive performance, ILDs show considerable promise for subadult classification, particularly when combining measurements from the facial skeleton and cranial base. These findings indicate that population-specific craniofacial variation is detectable across subadult development and that incorporating the full developmental sample may yield more accurate classifications than age-stratified approaches.

Interpreting skeletal trauma requires understanding how fracture outcomes reflect both extrinsic loading conditions and intrinsic biological variation. However, the contribution of biological variability to fracture behavior remains poorly defined, even under controlled loading conditions. This study examines how intrinsic factors influence fracture outcomes in human tibiae subjected to blunt impacts. Sixty human tibiae were experimentally loaded under consistent dynamic four‐point bending conditions. Fracture count and morphology were evaluated in relation to age, sex, body composition, and tibial geometry using descriptive, bivariate, and multivariate statistical approaches. Fracture outcomes ranged from simple to complex despite identical loading conditions. Fracture count showed limited association with biological or skeletal variables and was a limited standalone indicator of trauma severity under controlled conditions. Fracture morphology varied across sex and showed significant differences with BMI, weight, and age. Multivariate statistics identified clustering patterns not apparent in univariate analyses. These findings indicate that intrinsic biological variability contributes to fracture outcomes and that fracture morphology reflects the interaction of biological and mechanical factors and extrinsic loading. Together, these results show that both the individual and the event contribute to fracture morphology, limiting its use as a standalone indicator of force or injury mechanism in forensic interpretation.

Phenotypes are multidimensional. Yet many contemporary methodologies lack the capability to fully quantify the biological complexity of dynamic and multivariate processes. To address this, we fit a Bayesian Gaussian copula model with a diverse, high dimensional human growth and development dataset using traits related to growth of individual long bones, development and ossification of the skeleton, and development of the dentition. The results demonstrate a modular pattern of human growth and development with the strongest relationships between traits of similar ontogenetic origin. Relationships between traits are hierarchically organized within and between modules and can be further dissected into units related to growth direction, ontogenetic timing of skeletal fusion and dental development. We show that the results above can be translated to practical applications in biological anthropology such as the estimation of an unknown independent variable (e.g. forensic age estimation) or the estimation of a missing response variable (e.g. data imputation). Our understanding of the multivariate human growth and development phenotype is enhanced by this research, and it demonstrates the necessity of appropriate multivariate modelling in both inference and prediction tasks.

Objectives: This paper presents a metric methodology for estimating biological sex specifically tailored to the Italian population. The method considers 121 standard metric measurements derived from 46 bones across various post-cranial regions. Materials and methods: The sample consists of 400 individuals (M = 200; F = 200) from the 20th century CAL Milano Cemetery Skeletal Collection aged 20 to 104 years old. The sample was divided into a training subset (75%; n = 300) and a testing subset (25%, n = 100). Intra- and inter-observer analyses, as well as univariate sectioning points, and multivariable logistic regression analyses were performed. Results: Intra- and inter-observer analysis showed excellent reproducibility of the measurements, with some exceptions generally related to the measurement of long bone diameters. Univariate sectioning points resulted in 18 measurements with accuracies exceeding 90%, and another 48 measurements achieving over 80% accuracy. In total, 43 multivariable logistic regression models were developed for 32 bones, and these models further increased the accuracy. Discussion: The validation of these models demonstrated that the proposed methodology allows for sex estimation with accuracies of over or near 90% and minimal class discrimination bias across all post-cranial skeletal regions. The highest accuracies – with both sectioning points and multivariable models – were the radius (96.8%), scapula (95.3%), and tibia (95.2%). This study introduces a comprehensive metric standard for the Italian population and highlights the accuracy of the metric approach for estimating biological sex.

Background/Objectives: The incorporation of the human growth and development literature, an ontogenetic framework, a large virtual sample of individuals across the entire growth period, and a contemporary sample of adult individuals provides a unique opportunity to explore the cranial complex across the entire life cycle. This study (1) assesses cranial variation in postnatal ontogeny to determine the life history stage during which subadult crania can reach comparable levels of phenotypic expression to adult crania and (2) exposes when biological sex can be estimated using craniometric data from immature individuals with accuracy levels comparable to adults. Methods: Contemporary individuals between birth and 102 years of age from one virtual (Subadult Virtual Anthropology Database; SVAD) and one skeletal (Forensic Data Bank; FDB) collection were used in the analyses (n = 2152). Results: Discriminant analysis reveals a clear ontogenetic trajectory across the life history stages, with adolescents, SVAD adults, and FDB adults exhibiting similar cranial dimensions. The analysis also revealed a shift from the growth energetic period into the reproductive energetic period during adolescence. This transition is reflected in the divergence of male and female craniometrics in adolescence, which is also when sex estimation accuracy is comparable to SVAD and FDB adults. Conclusions: The current study argues that skeletal and/or dental maturity is not necessary to estimate sex using the cranium and urges the field to reconsider methodological divisions between subadults and adults.

Although cranial growth has been extensively explored, forensic and biological anthropology lack a formal incorporation of how cranial growth processes impact the adult phenotype and downstream biological profile estimations. Objectives: This research uses an ontogenetic framework to identify when interlandmark distances (ILDs) stabilize during growth to reach adult levels of variation and to evaluate patterns of cranial sexual size dimorphism. Methods: Multivariate adaptive regression splines (MARS) were conducted on standardized cranial ILDs for 595 individuals from the Subadult Virtual Anthropology Database (SVAD) and the Forensic Data Bank (FDB) aged between birth and 25 years. Cross-Validated R-squared (CVRSq) values evaluated ILD variation explained by age while knot placements identified meaningful changes in ILD growth trajectories. Results: Results reveal the ages at which males and females reach craniometric maturity across splanchnocranium, neurocranium, basicranium and cross-regional ILDs. Changes in growth patterns observed here largely align with growth milestones of integrated soft tissue and skeletal structures as well as developmental milestones like puberty. Conclusions: Our findings highlight the variability in growth by sex and cranial region and move forensic anthropologists towards recognizing cranial growth as a mosaic, continuous process with overlap between subadults and adults rather than consistently approaching subadult and adult research separately.

Revising methodologies is essential to understand the limitations and biases inherent in certain methods, which is crucial for obtaining reliable results. Due to the subjective nature of non‐metric methods, variation in trait scoring and its impact on accurately classifying biological parameters remains a concern that requires further investigation. This study aimed to examine the effects of observer experience, familiarity with the method, and different statistical approaches on the repeatability of macromorphoscopic traits in the cranium for population affinity. Seventeen traits were scored on a sample of 10 crania by five observers with varying experience levels. Intra‐observer agreement ranged from moderate to perfect, with three traits—inferior nasal margin, nasal bone shape, and nasal overgrowth demonstrating—the lowest agreement. Overall, inter‐observer repeatability ranged from poor to substantial agreement. After a group discussion on the scoring procedure and subsequent rescoring of the crania, a slight improvement in agreement was observed, with kappa values shifting towards moderate and substantial levels. Each observer exhibited variation in the repeatability of different traits. While general experience did not consistently translate into proficiency with the method, familiarity with the specific traits and scoring procedures contributed to more consistent results. Therefore, method‐specific training is crucial before applying the MMS traits in practice. Additionally, the choice of statistical approaches—such as applying different weights to Cohen's kappa based on data type—can influence the perceived reliability of a method. Practitioners should select weights and tests that are most appropriate for the data type of each trait being analyzed.

The present study introduces new regression formulae that address several challenges of current subadult stature estimation methods by 1) using a large, contemporary, cross-sectional sample of subadult skeletal remains; 2) generating regression models using both lengths and breadths; 3) utilizing both linear and nonlinear regression models to accommodate the nonlinear shape of long bone growth; and 4) providing usable prediction intervals for estimating stature. Eighteen long bone measurements, stature, and age were collected from computed tomography images for a sample of individuals ( n = 990) between birth and 20 years from the United States. The bivariate relationship between long bone measurements and stature was modeled using linear and nonlinear methods on an 80% training sample and evaluated on a 20% testing sample. Equations were generated using pooled-sex samples. Goodness of fit was evaluated using Kolmogorov–Smirnov tests and mean absolute deviation (MAD). Accuracy and precision were quantified using percent testing accuracy and Bland–Altman plots. In total, 38 stature estimation equations were created and evaluated, all achieving testing accuracies greater than 90%. Nonlinear models generated better fits compared to linear counterparts and generally produced smaller MAD (3.65 – 15.90cm). Length models generally performed better than breadth models, and a mixture of linear and nonlinear methods resulted in highest testing accuracies. Model performance was not biased by sex, age, or measurement type. A freely available, online graphical user interface is provided for immediate use of the models by practitioners in forensic anthropology and will be expanded to include bioarchaeological contexts in the future.

To date South African forensic anthropologists are only able to successfully apply a metric approach to estimate population affinity when constructing a biological profile from skeletal remains. While a non-metric, or macromorphoscopic approach exists, limited research has been conducted to explore its use in a South African population. This study aimed to explore 17 cranial macromorphoscopic traits to develop improved methodology for the estimation of population affinity among black, white and coloured South Africans and for the method to be compliant with standards of best practice. The trait frequency distributions revealed substantial group variation and overlap, and not a single trait can be considered characteristic of any one population group. Kruskal-Wallis and Dunn’s tests demonstrated significant population differences for 13 of the 17 traits. Random forest modelling was used to develop classification models to assess the reliability and accuracy of the traits in identifying population affinity. Overall, the model including all traits obtained a classification accuracy of 79% when assessing population affinity, which is comparable to current craniometric methods. The variable importance indicates that all the traits contributed some information to the model, with the inferior nasal margin, nasal bone contour, and nasal aperture shape ranked the most useful for classification. Thus, this study validates the use of macromorphoscopic traits in a South African sample, and the population-specific data from this study can potentially be incorporated into forensic casework and skeletal analyses in South Africa to improve population affinity estimates.