Search Results (161)

Saprophagic flies can have a vital role in forensic investigations of suspicious death by providing an estimate of the minimum post-mortem interval (minPMI). An exemplar blow fly species, Calliphora vicina (Diptera: Calliphoridae), is frequently reported as being of greatest importance in this role, but without significant corroborating evidence. Therefore, the present study examined 122 anonymised, historic cases of suspicious death investigated in Great Britain (GB) to determine the role of Diptera and of C. vicina in particular. Thirty-seven fly species in eighteen families were recorded, with greater diversity outdoors (17 families) than indoors (11 families). Family diversity and the age of the oldest developmental stage increased as estimated minPMI increased. Indoor minPMIs tended to be lower than outdoor minPMIs, probably due to the higher likelihood of finding a body indoors. The most commonly collected family and species were Calliphoridae and C. vicina, respectively, the latter recorded in 69.7% (85/122) of all cases. The four main reasons for the importance of C. vicina are: (1) widespread distribution; (2) broad seasonality; (3) low thermal tolerance; and (4) ability to colonise bodies soon after death. It was used to determine minPMI in 85.9% of cases in which it was collected (73/85). Where a most likely PMI could be compared by regression analysis with a minPMI from C. vicina evidence, the relationship was robust (R² > 0.98), accurate to within 2.5–2.7 days at a most likely PMI of 20 days. With greater research effort, Calliphora vicina and more neglected fly species can have an increasing role in future forensic investigations of suspicious death. Full article

Forensic age estimation is a fundamental component of biological profiling for unidentified skeletal remains, particularly in mass casualty incidents where specimens are frequently fragmented or incomplete. This review evaluates the diagnostic utility of craniofacial suture closure—specifically across four facial regions—as a non-invasive methodology for age determination in adults. By analyzing the predictable fusion patterns of ectocranial and endocranial sutures, forensic practitioners can derive approximate age ranges when postcranial indicators are absent or unreliable. Despite its utility, the reliability of suture-based estimation remains a subject of academic debate. The rate of closure is influenced by a complex interplay of environmental and biological factors, including nutritional status, hormonal influences, and mechanical loading. Historically, the method has faced criticism due to significant inter-individual variability and limited sample sizes in cadaveric studies. To improve precision and novel detail, this review explores the integration of emerging technologies such as artificial intelligence (AI) and machine learning (ML). These tools can process extensive cranial datasets to identify subtle morphological patterns that may elude human observation. While craniofacial suture analysis remains an essential resource in the forensic toolkit, its accuracy is contingent upon accounting for multi-factorial biological factors. The authors emphasize the necessity for further external validation across diverse global populations to ensure the generalizability and refinement of the technique in forensic medicine and osteology. Full article

Background/Objectives: Sex estimation from craniofacial morphology is a fundamental component of biological profile construction in forensic anthropology. Population-specific reference data for Thai individuals derived from computed tomography (CT) remain limited, and direct comparisons between discriminant function analysis (DFA) and machine learning classifiers are frequently complicated by inconsistent validation protocols. This study aimed to characterize sexual dimorphism in CT-derived craniofacial measurements, compare the classification performance of DFA, support vector machine (SVM), and random forest (RF) under a unified validation protocol, and demonstrate their practical application in a forensic context. Methods: CT images from 300 Thai adults (150 males, 150 females; age range 20–90 years) were obtained from Srinagarind Hospital, Khon Kaen University. Eight linear craniofacial measurements spanning the cranial vault, facial skeleton, nasal aperture, and orbital region were obtained from each case. DFA, SVM, and RF were developed and compared under a unified leave-one-out cross-validation protocol. Classification performance was assessed using accuracy, AUC, and Matthews correlation coefficient (MCC). Results: Seven of eight measurements exhibited statistically significant sexual dimorphism, with facial breadth and nasal height demonstrating the greatest dimorphism. DFA achieved the highest classification accuracy of 85.7%, AUC of 0.924, and MCC of 0.713, incorporating five measurements into the canonical function. SVM and RF achieved comparable accuracy of 84.7% and 84.0%, respectively. All three classifiers correctly classified both forensic application cases with high confidence. Conclusions: CT-derived craniofacial measurements provide a reliable basis for sex estimation in Thai adults. The convergence of performance across all three classifiers under a unified internal validation protocol strengthens confidence in the internally validated performance estimates. The derived discriminant function equation and saved machine learning models constitute a complementary and immediately applicable toolkit for CT-based forensic sex estimation in the Thai population. Full article

Background: While human cranial sutures are well-established indicators for age-at-death estimation in forensic anthropology, facial sutures remain an underutilized resource despite their critical role in facial growth and development. Macroscopic examination of craniofacial suture closure patterns reflects physiological aging processes and can provide valuable information at crime scenes. This study aimed to address the gap of knowledge by quantitatively evaluating the efficacy of facial suture closure patterns for age estimation. Methods: A sample consisting of 296 Thai skulls was analyzed to assess facial suture closure based on anatomical morphology. The sutures were evaluated using various established classification systems to determine the most effective method for predicting age ranges. To ensure consistency and reliability, the evaluations were conducted by three independent raters. Results: The assessment demonstrated good Intraclass Correlation (ICC = 0.755, df = 14, p < 0.05). Among the classification methods tested, the Modified Meindl and Lovejoy Scoring System yielded the highest sensitivity, ranging from 90.9% to 100% in males and 75.4% to 96.1% in females. Specifically, the zygomaticomaxillary suture showed the highest sensitivity in males, whereas the frontonasal and sphenozygomatic sutures were the most sensitive indicators in females. Utilizing the total sum score (TSS), the following sex-specific linear regression formulas for age-at-death were generated: (Males: Age-at-death = 1.7625(TSS) − 17.094. Females: Age-at-death = 1.7325(TSS) − 12.865). Conclusions: Facial sutures exhibit distinct, sex-specific closure patterns that serve as robust and reliable indicators for estimating age, with higher sensitivity generally observed in males. The utility of this novel method is heavily dependent on the scoring system employed, highlighting the critical importance of utilizing modified, sex-specific analyses. While these population-specific models tailored to the Thai demographic effectively refine age estimation outcomes, integrating this methodology with broader biological profiling remains essential for high-confidence forensic identification. Full article

Age estimation is an important component of forensic investigation, with applications in criminal casework, immigration assessments, and disaster victim identification. Determining whether an individual is a minor or an adult, or estimating the age at death of unidentified remains, can have significant legal and humanitarian implications. Traditional forensic age estimation methods rely primarily on anthropological and radiological assessment of skeletal development and degeneration; however, these approaches may be limited by subjectivity, population-specific reference standards, and reduced precision in adult age estimation. In recent years, molecular biomarkers have emerged as promising complementary tools for age prediction. Molecular approaches, including DNA methylation profiling, Y-chromosome-associated markers, RNA-based biomarkers, mitochondrial DNA alterations, proteomic signatures, and telomere length analysis, reflect biological processes associated with aging and may provide objective indicators that can be measured from biological samples. Among these methods, DNA methylation-based models currently demonstrate the strongest predictive performance and represent the most extensively studied molecular strategy for forensic age estimation. Nevertheless, several challenges remain before widespread forensic implementation can be achieved, including tissue specificity, environmental influences on biomarker stability, population variability, and the need for robust validation across laboratories and forensic sample types. This review summarises the current molecular approaches investigated for forensic age estimation, evaluates their biological basis and methodological limitations, and discusses their potential integration into forensic workflows. While molecular techniques offer promising avenues for improving age estimation, further standardisation, validation, and careful interpretation are required before they can be routinely applied in forensic practice. Full article

DNA methylation (DNAm) age estimation is one of the hottest topics in forensic contexts. However, there is growing evidence that DNAm can be affected by several factors, including many clinical conditions. In this study, we analyzed the methylation levels within the FHL2 gene in Portuguese women using the droplet digital PCR (ddPCR) methodology to develop age prediction models (APMs). We hypothesized that obesity could affect the accuracy of APMs and would be associated with the advancement in epigenetic aging. We collected blood samples from 62 women (aged 21–58 years old) with overweight and obesity. DNA extracts were subjected to bisulfite conversion followed by ddPCR using dual-labeled probes targeting the methylated and unmethylated FHL2 CpG site cg06639320. The developed APM yielded a mean absolute deviation (MAD) of 4.72 years between predicted and chronological ages in the total sample. When applying the developed APM to women classified as overweight, the MAD was 3.64 years, while, for those with obesity class 1, it was 3.93 years, and, for those with obesity class 2, 6.29 years. The same pattern of accuracy was observed when we developed APMs specifically for the groups categorized by overweight and obesity, obtaining MAD values of 3.75 years (overweight), 3.69 years (obesity class 1) and 6.24 years (obesity class 2). Our study indicates that severe obesity may impact the accuracy of DNA methylation-based age estimators. We did not find evidence of an association between BMI and accelerated epigenetic aging. However, we found signals of epigenetic age acceleration in younger subjects and epigenetic age deceleration in the older participants. Full article

Background/Objectives: Calcaneal spurs are pathological bone formations at entheseal attachment sites with clinical implications but limited forensic anthropological applications. While entheseal changes have been proposed as age estimation markers in forensic contexts, empirical validation remains insufficient, particularly for Southeast Asian populations. This study evaluated calcaneal spur utility for forensic age estimation in Thai skeletal remains while establishing population-specific osteological reference data for forensic individuation. Materials and Methods: The 3516 dry calcanei from 1758 Northeastern Thai skeletons (1031 males, 727 females; age 22–106 years) were examined. Spurs were classified by anatomical location as dorsal (D-type), plantar (P-type), or combined plantar–dorsal (P–D type). The morphometric measurements were performed bilaterally. Age-associated patterns were analyzed across four age cohorts (≤40, 41–50, 51–60, ≥61 years), and Random Forest machine learning classification tested forensic age estimation capacity using 10-fold cross-validation. Results: Overall prevalence reached 67.63% with distinctive P–D type predominance. While age-stratified prevalence increased from 24.56% (≤40 years) to 74.77% (≥61 years), Random Forest modeling explicitly demonstrated overall classification accuracy of 62.5%. Compared between sexes, the maximum length of calcaneal spurs was significantly longer in males. Dimensional analyses revealed weak age correlations and substantial inter-individual morphological variation precluded reliable age prediction. Interestingly, the unique P–D type distribution pattern (77.5% among spur-bearing individuals) may serve as an auxiliary marker for Thai population affinity assessment in forensic contexts. Conclusions: This study established the first comprehensive Thai-specific osteological reference for calcaneal spurs, revealing distinctive plantar–dorsal type predominance valuable for forensic population affinity assessment and provided population-specific baseline data for forensic individuation. Full article

Dental hard tissues, through their remarkable resistance to degradation, represent one of the most durable biological materials available for postmortem investigation. The preparation of undecalcified or ground sections allows microscopic visualization of enamel, dentin and cementum structures, which can preserve chronological, physiological, or environmental information. This review provides a comprehensive overview of the forensic applications of dental hard tissue ground sections, focusing on methodological principles, interpretive potential and practical constraints. The literature in forensic odontology highlights their relevance for age estimation through tooth cementum annulation, identification of neonatal and accentuated stress lines, and the assessment of thermal or chemical alterations. While these methods have proven scientific validity in anthropology and histology, their forensic implementation remains limited by heterogeneity in protocols and interpretative subjectivity. Standardization of preparation techniques, digital imaging, and integration with complementary analyses such as micro-CT or SEM could enhance the reliability and medico-legal relevance of this classical but underused approach. Full article

Objectives: To develop and prospectively validate a predictive model to estimate the fetal postmortem interval (PMI) using three-dimensional postmortem ultrasound (3D PM-US) measurements of corneal and ocular volumes. Methods: Single-center study including fetuses ≥ 20 weeks’ gestation with known time of death after feticide. A retrospective training cohort (n = 63; November 2022–July 2023) and a prospective validation cohort (n = 28; February–August 2025) were used. Corneal and ocular volumes were measured using the VOCAL™ rotation multiplanar technique; the cornea-to-eyeball volume ratio was calculated for each case. Automated machine learning (AutoML) was used to develop and validate a gradient boosting machine (GBM) model. Model performance was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). Results: Ninety-four fetuses were included; three were excluded (two for extreme death–US intervals of 165 and 166 h; one for open eyelids). Median gestational age was 29.3 weeks (IQR 27.2–32.9); median birthweight was 1325 g (IQR 980–1880). The cornea-to-eyeball volume ratio was an independent predictor of PMI (p < 0.001). The GBM model explained 91% of the variance in the training cohort (R² = 0.91, RMSE = 11.49 h, MAE = 8.45 h) and 75% in the validation cohort (R² = 0.75, RMSE = 18.32 h, MAE = 14.49 h), demonstrating strong predictive accuracy and minimal overfitting. Variable importance analysis confirmed the cornea-to-eyeball ratio as the most influential and biologically plausible predictor of PMI. A Shiny web application was developed to facilitate clinical implementation. Conclusions: 3D PM-US measurements of the fetal cornea and eyeball can reliably and quantitatively estimate the PMI with good predictive accuracy using a GBM model. Multicenter studies are required to further refine the model, enable external validation, and determine its clinical and forensic utility. Full article

The reconstruction of biological profiles from aged or degraded human skeletal remains represents a major challenge in both forensic and bioarcheological contexts, particularly when conventional identification approaches fail. Recent advances in molecular genetics, biochemical and histological analyses, and biomolecular anthropology have substantially expanded the range of information that can be recovered from compromised remains. This review synthesizes current integrative approaches combining genomic analyses, stable isotope investigations, epigenetic age estimation, proteomic sex determination, and complementary histological techniques to infer sex, ancestry, kinship, age, diet, mobility, and geographic origin. Genetic methods, including next-generation sequencing (NGS), enable increasingly robust inference even from highly degraded samples. Stable isotope analyses provide insights into dietary patterns and mobility, while DNA methylation markers improve age estimation accuracy. Tooth cementum annulation (TCA), although a histological rather than molecular method, contributes an additional chronological indicator within an integrative analytical framework. Rather than treating these approaches independently, this review proposes a multidisciplinary perspective in which complementary datasets collectively support biological profile reconstruction. Integrative interpretation enhances identification potential and provides more nuanced life-history reconstructions, demonstrating the value of combining molecular, biochemical, and histological evidence in forensic and archaeological investigations. Full article

In forensic pathology, accurately estimating the time since injury is essential. Current histological and imaging approaches commonly miss subtle temporal changes, especially in deaths occurring within hours of injury. This review discusses the timing of neuroinflammation after traumatic brain injury and emphasizes possible markers for estimating the time of injury in forensic cases. Promising markers include microglial activation (allograft inflammatory factor 1 and transmembrane protein 119, detectable within 10 min to 2 h), β-amyloid precursor protein accumulation (20–35 min), high-mobility group box 1 translocation (2–6 h), cytokine fluctuations (IL-1β and TNF-α peak between 4 and 24 h, IL-6 shows delayed, extended elevation), sequential leukocyte infiltration (neutrophils from 2 to 48 h, lymphocytes after 3–5 days), blood–brain barrier breakdown markers such as fibrinogen and IgG leakage, loss of tight junction proteins (2–3 h), matrix metalloproteinase-9 activity (peaking at 24–48 h), and reactive astrocytosis with increased glial fibrillary acidic protein levels (from 12 to 24 h onward). The association between injury severity and inflammation is influenced by factors such as age, genetics (e.g., APOE ε4), coexisting conditions, and preexisting inflammation, which reduce the reliability of individual markers. A multiparametric approach may offer the best prospects to improve the accuracy of post-traumatic and post-mortem interval assessment in medicolegal cases. Full article

Background/Objectives: DNA profiling in forensic investigation typically compares genetic profiles, usually derived from the analysis of STR markers. However, this method has limitations when there is no biological reference sample or match in the DNA database. The aim of the current study is thus to replicate, in an Italian cohort, epigenetic markers previously identified in the literature for distinguishing tobacco smokers from non-smokers or estimating chronological age, so as to help narrow down the pool of suspects. Methods: DNA methylation at four CpG dinucleotides located around the cg05575921 site of the AHRR gene, widely associated with tobacco consumption, was measured. Additionally, five CpG dinucleotides in the ELOVL2, FHL2, KLF14, TRIM59, and C1orf132 genes were examined for chronological age estimation in buccal swab samples of 102 volunteers through pyrosequencing. Results: A multiple linear regression model for estimating chronological age shows that ELOVL2-C7, C1orf132-C1, and TRIM59-C7 have a significant effect on age. In this model, the prediction error increases with age. Two logistic regression models were used for determining smoker/non-smoker status, proving that two CpG sites significantly influence the odds of being classified as a smoker. When ex-smokers are included in the non-smoking group, the model correctly classifies the two conditions in about 80% of cases. Conclusions: The results demonstrate that the models generated from pyrosequencing data are useful for identifying tobacco smokers and estimating an individual’s chronological age, particularly for younger subjects. Further studies are needed to develop models with higher predictive accuracy and to integrate these tools into regular forensic practice. Full article

Accurate estimation of hematoma age remains a major challenge in forensic practice, as current assessments rely heavily on subjective visual interpretation. Hyperspectral imaging (HSI) captures rich spectral signatures that may reflect the biochemical evolution of hematomas over time. This study evaluates whether a convolutional neural network (CNN) integrating both spectral and spatial information improves hematoma age estimation accuracy. Additionally, we investigate whether performance can be maintained using a reduced, physiologically motivated subset of wavelengths. Using a dataset of forearm hematomas from 25 participants, we applied radiometric normalization and SAM-based segmentation to extract $64\times 64\times 204$ hyperspectral patches. In leave-one-subject-out cross-validation, the CNN outperformed a spectral-only Lasso baseline, reducing the mean absolute error (MAE) from 3.24 days to 2.29 days. Band-importance analysis combining SmoothGrad and occlusion sensitivity identified 20 highly informative wavelengths; using only these bands matched or exceeded the accuracy of the full 204-band model across early, middle, and late hematoma stages. These results demonstrate that spectral–spatial modeling and physiologically grounded band selection can enhance estimation accuracy while significantly reducing data dimensionality. This approach supports the development of compact multispectral systems for objective clinical and forensic evaluation. Full article

The application of advanced imaging techniques, particularly computed tomography (CT), photogrammetric scanning, and three-dimensional reconstructions of body surfaces and skeletal remains, is becoming a crucial component of Forensic Anthropology. These tools enable a non-invasive and highly standardized analysis of both intact cadavers and human remains recovered from terrestrial or aquatic environments, providing reliable support in identification processes, traumatological reconstruction, and the assessment of taphonomic processes. In the context of estimating the Post-Mortem Interval (PMI) and the Post-Mortem Submersion Interval (PMSI), digital imaging allows for the objective and reproducible documentation of morphological changes associated with decomposition, saponification, skeletonization, and taphonomic patterns specific to the recovery environment. Specifically, CT enables the precise assessment of gas accumulation, transformations in residual soft tissues, and structural bone modifications, while photogrammetry and 3D reconstructions facilitate the longitudinal monitoring of transformative processes in both terrestrial and underwater contexts. These observations enhance the reliability of PMI/PMSI estimates through integrated models that combine morphometric, taphonomic, and environmental data. Beyond PMI/PMSI estimation, imaging techniques play a central role in anthropological bioprofiling, facilitating the estimation of age, sex, and stature, the analysis of dental characteristics, and the evaluation of antemortem or perimortem trauma, including damage caused by terrestrial or fauna. Three-dimensional documentation also provides a permanent, shareable archive suitable for comparative analyses, ensuring transparency and reproducibility in investigations. Although not a complete substitute for traditional autopsy or anthropological examination, imaging serves as an essential complement, particularly in cases where the integrity of remains must be preserved or where environmental conditions hinder the direct handling of osteological material. Future directions include the development of AI-based predictive models for PMI/PMSI estimation using automated analysis of post-mortem changes, greater standardization of imaging protocols for aquatic remains, and the use of digital sensors and multimodal techniques to characterize microstructural alterations not detectable by the naked eye. The integration of high-resolution imaging and advanced analytical algorithms promises to further enhance the reconstructive accuracy and interpretative capacity of Forensic Anthropology. Full article

Background/Objectives: Bone age assessment is critical in pediatric endocrinology and forensic medicine. Although recently developed multimodal large language models (LLMs) show potential in medical imaging, their diagnostic performance in bone age determination has not been sufficiently evaluated. This study evaluates the performance of four multimodal LLMs (ChatGPT-5, Gemini 2.5 Pro, Grok-3, and Claude 4 Sonnet) in bone age determination using the Gilsanz–Ratib (GR) atlas. Methods: This retrospective study included 245 pediatric patients (109 male, 136 female) under the age of 18 who underwent left wrist radiography. Each model estimated bone age using the patient’s radiograph and GR atlas as reference (atlas-assisted prompting). Bone age assessments made by an experienced radiologist using the GR atlas were evaluated as the reference standard. Performance was assessed using mean absolute error (MAE), intraclass correlation coefficient (ICC), and Bland–Altman analysis. Results: ChatGPT-5 demonstrated statistically superior performance, with an MAE of 1.46 years and ICC of 0.849, showing the highest alignment with the reference standard. Gemini 2.5 Pro showed moderate performance, with an MAE of 2.24 years; Grok-3 (MAE: 3.14 years) and Claude 4 Sonnet (MAE: 4.29 years) had error rates that were too high for clinical use. Conclusions: Significant performance differences exist among multimodal LLMs, despite atlas-supported prompting. Only ChatGPT-5 qualified as “clinically useful,” demonstrating potential as an auxiliary tool or educational support under expert supervision. Other models’ reliability remains insufficient. Full article