Estimating the Minimum Number of Individuals (MNI) in commingled skeletal assemblages is a core task in forensic and archaeological anthropology. Traditional methods, based on the frequency of lateralized elements, often suffer from underestimation due to fragmentation and morphological similarity. This study introduces a new MNI estimation method grounded in the principle of certain exclusion (with a 99% confidence interval), which focuses on detecting osteometric incompatibilities between elements rather than identifying matches. We developed linear regression models based on a large international dataset (n = 2,969 individuals) to generate 99% prediction intervals between long bone measurements. A recursive protocol, termed Allometric Research by Exclusion (RAE), was implemented to isolate bones that cannot belong to the same individual. The method was validated via bootstrap simulations using independent samples from the Milano and Pretoria Bone collections. Across all assemblages, the exclusion-based MNI estimates were consistently higher than those obtained by frequency-based methods, and more closely approximated the real number of individuals. This trend was especially clear in small to mid-sized assemblages, where precise MNI estimation is most critical. The exclusion method offers a statistically grounded, replicable, and conservative approach to MNI estimation. Its integration into forensic and archaeological workflows may enhance the accuracy and defensibility of population reconstruction in complex contexts.

Sex estimation represents a fundamental step in forensic identification protocols, traditionally relying on morphoscopic pelvic assessment. However, the increasing integration of machine learning approaches and population-specific validation requirements necessitate comprehensive evaluation of alternative methodologies. This study provides the first direct comparison of established morphoscopic methods (Phenice 1969, Bruzek 2002) against a multivariable long bone linear discriminant analysis (LDA) model in a sample of 333 documented individuals from the CAL Milano Cemetery Skeletal Collection. Metric data were preprocessed to address missing values through imputation prior to analysis. The morphoscopic methods achieved high accuracy rates (Phenice: 96.9%, Bruzek: 97.7%) but showed significant exclusion rates due to preservation limitations (32.4% and 2.4% respectively). The long bone LDA model demonstrated comparable performance with threshold-dependent accuracy ranging from 95.2% (0.50 threshold) to 98.1% (0.88 threshold), while maintaining universal applicability across all specimens. Crucially, disagreement analysis revealed method-specific error patterns with minimal overlap in misclassified individuals, supporting complementary rather than redundant diagnostic signals. These findings validate long bones as a reliable alternative for sex estimation in fragmentary remains while establishing population-specific accuracy benchmarks for contemporary Italian forensic applications. The threshold-adjustable probabilistic framework offers operational flexibility for balancing classification certainty against sample coverage requirements.

The field of anthropometry is crucial in developing methods for reconstructing the biological profile of skeletal remains. Ensuring the accuracy and reliability of osteometric data is therefore essential for methodological validation. This study assesses the repeatability and inter-observer reliability of skeletal measurements collected for the publication of the Olivier Collection. Four observers recorded 42 linear variables (11 cranial and 31 postcranial) on 40 randomly selected skeletons. Inter-observer variation was evaluated through Student’s t-tests, Cohen’s d, repeated-measures ANOVAs, intraclass correlation coefficients (ICCs), and both absolute and relative technical errors of measurement (TEM, rTEM). Additionally, a new hierarchical linear model method was used to partition measurement variance into components attributable to subjects, systematic and random measurement error or systematic effects associated with sex.Significant systematic measurement effects (p

Background Age estimation in living individuals is a medico-legal procedure of paramount importance, particularly in French and European legal contexts. It is decisive for establishing identity and applying legal frameworks, notably for Unaccompanied Minors (UAMs) whose minority status confers specific fundamental rights. However, the reliability of age estimation methods and the heterogeneity of medico-judicial practices raise major challenges. Objective This article aims to provide an overview of age estimation practices in living individuals in France and Europe, identifying scientific, legal, and ethical gaps, and proposing concrete recommendations for harmonization and improvement of these procedures. Methods A comprehensive review of relevant scientific literature, legislative texts, and institutional reports in France and several European countries (Germany, Belgium, Spain, Italy, Netherlands, United Kingdom, Switzerland) was conducted. The analysis focused on medical methods used (bone and dental imaging), legal frameworks (presumption of minority, consent, probative value of examinations), and challenges encountered by involved professionals. Results Age estimation practices in France and Europe are characterized by significant heterogeneity, often subsidiary recourse to medical examinations, and scientific limitations inherent to current methods (imprecision, error margins, individual and population variability, non-therapeutic nature of radiation). The presumption of minority, although protective, is applied inconsistently, and inter-agency coordination is often insufficient. These dysfunctions result in legal uncertainties and risks to the fundamental rights of UAMs. Conclusion Age estimation in living individuals, particularly UAMs, requires harmonization of protocols, adoption of an integrated multidisciplinary approach, rigorous quantification of result uncertainty, and strengthening of practitioner training. These developments are essential to ensure fair, reliable, ethical procedures that respect children's rights, in accordance with international obligations.

Forensic anthropology is a specialised field of biological anthropology that applies skeletal analysis and archaeological techniques within a legal and humanitarian context, mainly to contribute to identification of the deceased. In this paper we provide a brief overview of how forensic anthropology is organised in the Nordic countries, and the main tasks performed by forensic anthropologists. These include the analysis of human skeletal remains, particularly aiming at constructing a biological profile of the deceased. We touch upon recent developments in the field regarding the interpretation and understanding of bone trauma and the use of modern medical imaging technologies.

The case report is the primary means by which forensic anthropologists communicate the outcomes of their examination. Despite their central role, considerable variability currently exists in the content, structure, and clarity of such reports. Drawing on published guidelines, forensic science standards, and the collective experience of an international group of practitioners from the Board of the Forensic Anthropology Society of Europe (FASE), this paper presents practical recommendations for forensic anthropology reporting. It outlines the fundamental aims and principles of reporting, identifies essential report elements, and distinguishes minimum requirements from discretionary components. Emphasis is placed on scientific integrity, transparency of methods and reasoning, and clear communication to a diverse audience. These recommendations are meant to be applicable across jurisdictions and professional contexts, including independent practice, while remaining aligned with contemporary forensic science standards. By promoting consistency and clarity in reporting, these recommendations aim to support the reliability, interpretability, and admissibility of forensic anthropological evidence, and to strengthen the contribution of forensic anthropology to medico-legal investigations.

Background/Objectives: Sex estimation is a fundamental component of the forensic biological profile, and the pelvis is widely regarded as the most sexually dimorphic skeletal element. This study aimed to quantify pelvic sexual dimorphism in a contemporary Turkish sample using computed tomography (CT)-derived morphometric measurements, and to evaluate the performance of several machine learning (ML) algorithms for sex classification. Methods: Fourteen pelvic measurements were obtained from CT reconstructions of 201 individuals (101 males, 100 females) by a single observer; a stratified subset of 30 cases (15 males, 15 females) was remeasured by the same observer and by a second observer to assess intra- and inter-observer reliability. Four supervised ML classifiers—logistic regression, linear discriminant analysis (LDA), random forest, and support vector machine (SVM)—were trained and evaluated using stratified 10-fold cross-validation and an independent 30% hold-out test set. Results: Thirteen of the fourteen measurements differed significantly between sexes (p < 0.05). Intra-observer reliability was excellent for all 14 measurements (ICC = 0.943–0.998), and inter-observer reliability was good to excellent (ICC = 0.834–0.996), with the lowest value observed for pubic length. Cross-validated classification accuracy ranged from 98.0% (random forest) to 99.0% (logistic regression, LDA, and SVM), with corresponding cross-validated AUC values of 0.991–0.994, with logistic regression, LDA, and SVM each achieving 100% accuracy (AUC = 1.000) on the independent hold-out set (random forest: 96.7%, AUC = 0.999). Acetabular width was identified as the single most informative predictor (univariate area under the curve = 0.972), followed by acetabular height, the subpubic angle, ischial length, the transverse pelvic outlet, and the angle of the greater sciatic notch. Conclusions: These findings demonstrate that CT-derived pelvic morphometrics, combined with machine learning classification, provide a highly accurate and reproducible method for sex estimation in a Turkish forensic context, and support the development of population-specific standards for forensic anthropological casework, pending independent external validation; the classification and effect-size estimates reported here were, however, stable under repeated cross-validation and hyperparameter optimisation.

Background Sex estimation from human skeletal remains is a cornerstone of forensic anthropological analysis. Long bones, despite exhibiting less pronounced dimorphism than pelvis, serve as invaluable substitutes. However, traditional statistical approaches for sex estimation from long bone measurements often lack the precision and case-specific reliability demanded by stringent legal standards. This study addresses these critical limitations by rigorously exploring the potential of machine learning (ML) to significantly enhance sex estimation from long bones. Methods We analyzed 16 osteometric measurements from the humerus, radius, femur, and tibia of 2969 individuals (1207 females, 1762 males) across eight skeletal collections. Eleven ML algorithms were trained and cross-validated, then validated on an independent South African sample. To address the common issue of incomplete remains, we developed an “accuracy x-factors” approach. This method simulates missing data scenarios and selects tailored training subsets, yielding individualized reliability assessments adapted to specific measurement availability. Results Linear Discriminant Analysis (LDA) consistently achieved the highest performance, with accuracies up to 93%. The “accuracy x-factors” approach proved effective in providing per-individual confidence measures, highlighting that prediction reliability varies with data completeness. Adjusting thresholds to higher confidence levels (e.g., >0.7) substantially reduced error rates, allowing a conservative yet legally robust classification of a smaller but more reliable subset of cases. Conclusion ML offers a powerful framework for sex estimation from long bones. The proposed “accuracy x-factors” approach introduces a significant methodological advance by delivering transparent, case-specific confidence levels. This strengthens both the forensic applicability and the legal admissibility of long bone-based sex estimation.

Avalanches are a major cause of death in mountainous regions, primarily from asphyxia. However, increased recreational activities and climate change may be leading to more traumatic injuries, such as bone fractures, which are currently understudied. This study compared two distinct bone fracture classification systems, to better understand specific injury mechanisms in avalanche victims.We conducted a retrospective analysis of post-mortem CT scans from 13 adult avalanche victims in Grenoble, France, all with at least one bone fracture. Using MIP, MPR, and 3D reconstructions, we systematically classified fractures across ten major anatomical regions, representing the entire body. We analyzed each fracture to determine its traumatic mechanism using both the surgical AO/OTA and the anthropological Galloway et al. (2014) classification systems.The study included 13 individuals (61.5% male; mean age: 37 years), with a total of 265 fractured bones. Fractures were most frequently observed in the thorax (52%), spine (21%), and skull (14%). We found that multiple injury mechanisms, such as impact and compression, often occurred simultaneously. Both classification systems consistently identified six "burst"-type spinal fractures. However, for five open-book pelvic fractures, only the Galloway et al. system precisely described the specific injury mechanism.Our findings indicate that the AO/OTA and Galloway et al. classifications are complementary. The AO/OTA system offers standardized clinical utility, while the Galloway et al. system enhances forensic and anthropological interpretation by elucidating trauma mechanisms. These preliminary insights into bone injury mechanisms in avalanche events emphasize the need for interdisciplinary approaches to improve victim care and safety.

Sex estimation is an essential task in forensic anthropology. It is not only crucial for the identification of individuals from skeletal remains, but it is also essential for improving the reliability of other methods of biological profile estimation, such as age and stature, some of which perform better when sex is taken into account. This study investigates the application of machine learning (ML) techniques to sex estimation, with a particular focus on interpretability to address the "black box" challenge inherent in AI models. Using a diverse dataset of long bone measurements from 2,969 individuals, 12 different ML algorithms were evaluated. Missing data were handled using iterative regression imputation, though challenges arising from incomplete datasets underscored the need for improved data handling strategies. Linear Discriminant Analysis (LDA) emerged as the most accurate approach, achieving 95.2% accuracy. A key feature of this study is the integration of SHapley Additive exPlanations (SHAP) values, which provide individualized insights into the factors influencing each prediction. This interpretability framework ensures transparency and addresses legal and scientific concerns about the admissibility of AI-generated evidence in court. Indeed, misclassifications possibilities highlight the importance of clear, understandable models in forensic applications. The study emphasizes the significance of individualized prediction, illustrated by the probability of male or female classification for each individual, as well as the impact of missing values on prediction accuracy. This research demonstrates that ML models can effectively balance accuracy with interpretability, offering personalized, actionable insights for forensic investigations. It paves the way for AI-driven methods that meet both scientific rigor and legal standards, transforming sex estimation in forensic science by providing individualized, defensible evidence suitable for court.

The discrimination between falls and blows is an important task in forensic anthropology and pathology. This research aimed to test a discrimination method between falls and blows. This method was created from the quotation of 549 types of fractures for 57 bones and 12 anatomical regions. Different models were tested according to the sensibility of random forest parameters and their effects on model accuracies. The best model was based on binary coding of 12 anatomical regions or 28 bones with or without baseline (age and sex). We tested this new method in the distinction between falls and blows on post-mortem computerized tomography scans (PMCT). The sample was composed of 47 subjects with 36 falls and 11 blows, whose aetiologia was based on forensic reports. Of the 47 bodies, 35 were complete, and 12 presented missing bones; 39 were estimated to be falls and 8 to be blows. Of the 12 individuals with missing bones, 11 had a reasonable estimation of the etiology of fractures, i.e., 91.7%. Methods showed excellent etiology estimation for fall cases (97.2%) but misclassified 36.4% of blow cases. Our method misclassified 5 subjects (10.6%), more precisely, 4 blows and 1 fall. Overall, the reliability of the estimation of the etiology is substantial, with a Cohens k-values of 0.67. The method could be used in distinguishing between blows and falls and is also suitable for fragmented or missing bones. To ensure an easy and fast use of this method, we have developed a freely available online automated tool (http://fracture.cloud).

PURPOSE:Upstanding posture depends on the balance between the pelvis and spine, with minimal energy expenditure when spinal segments are aligned. Pelvic incidence (PI), a key measure of sagittal balance, is a constant individual characteristic that correlates with lumbar lordosis (LL) in adults. While sagittal balance has been widely studied in adults, there is limited research on the pediatric population, particularly pre-walking children and fetuses. METHODS:This study aimed to describe the development of PI and LL in healthy fetuses and children under 10 years old using MRI measurements. A retrospective analysis of MRI images from 96 subjects (20 fetuses and 76 children) undergoing MRI for non-spinal conditions was conducted. PI, wedging lumbar vertebral body angles (WVB) of L1-L5, and the sum of all WVB angles (SLL) were measured to assess their development and correlation with age. RESULTS:Results showed a slight positive correlation between PI and age, with a significant increase occurring during the acquisition of bipedalism. LL also increased with age, with notable postnatal development continuing into early childhood. Structural lordosis in the L4 and L5 vertebrae was evident in fetuses, indicating the presence of inherent lordosis in utero. CONCLUSION:While PI exhibited minor changes after birth, LL development was strongly influenced by biomechanical factors associated with growth and bipedalism. These findings improve our understanding of the evolution of spino-pelvic anatomy and could guide therapeutic approaches for pediatric spinal deformities. Further longitudinal studies are needed to explore the genetic and biomechanical determinants of PI and LL development.

INTRODUCTION:The morphological assessment of the pubic symphysis using the Suchey-Brooks method is considered a reliable age at death indicator. Age at death estimation methods can be adapted to the images obtained from post-mortem computed tomography (PMCT). The aim of this study is to evaluate the utility of pubic symphysis photorealistic images obtained through Global illumination rendering (GIR) for age at death estimation from whole-body PMCT and from focused PMCT on the pubic bone.MATERIALS AND METHODS:We performed virtual age at death estimation using the Suchey Brooks method from both the whole-body field of view (Large Field of View: LFOV) and the pubis-focused field of view (Small and Field of View: SFOV) of 100 PMCT. The 3D photorealistic images were evaluated by three forensic anthropologists and the results were statistically evaluated for accuracy of the two applied PMCT methods and the intra- and inter-observer errors.RESULTS:When comparing the two acquisitions of PMCT, the accuracy rate reaches 98.5% when using a pubic-focused window (SFOV) compared to 86% with a whole-body window (LFOV). Additionally, the intra- and inter-observer variability has demonstrated that the focused window provides better repeatability and reproducibility.CONCLUSION:Adding a pubic-focused field of view to standard PMCT and processing it with GIR appears to be an applicable technique that increases the accuracy rate for age at death estimation from the pubic symphysis.