Background: Identifying skeletal remains has been and will remain a challenge for forensic experts and forensic anthropologists, especially in disasters with multiple victims or skeletal remains in an advanced stage of decomposition. This study examined the performance of two machine learning (ML) algorithms in predicting the person’s sex based only on the morphometry of L1–L5 lumbar vertebrae collected recently from Romanian individuals. The purpose of the present study was to assess whether by using the machine learning (ML) techniques one can obtain a reliable prediction of sex in forensic identification based only on the parameters obtained from the metric analysis of the lumbar spine. Method: This paper built and tuned predictive models with two of the most popular techniques for classification, RF (random forest) and XGB (xgboost). Both series of models used cross-validation and a grid search to find the best combination of hyper-parameters. The best models were selected based on the ROC_AUC (area under curve) metric. Results: The L1–L5 lumbar vertebrae exhibit sexual dimorphism and can be used as predictors in sex prediction. Out of the eight significant predictors for sex, six were found to be particularly important for the RF model, while only three were determined to be important by the XGB model. Conclusions: Even if the data set was small (149 observations), both RF and XGB techniques reliably predicted a person’s sex based only on the L1–L5 measurements. This can prove valuable, especially when only skeletal remains are available. With minor adjustments, the presented ML setup can be transformed into an interactive web service, freely accessible to forensic anthropologists, in which, after entering the L1–L5 measurements of a body/cadaver, they can predict the person’s sex.

Introduction: Forensic identification practice requires a multitude of methods for positive identification, among which the facial reconstruction occupies a high place. For this to be done in the best way possible, it is mandatory that each population have their own database regarding soft tissue thickness. Objective: The present research is focused on creating such a database for the Romanian adult population and on exploring the statistical distribution and correlation of craniometric landmarks, overall, and in groups defined according to sex and weight categories. This might provide support in estimating the values of missing landmarks or other variables (e.g., age, sex) for unidentified bodies. Methods: Twelve craniometric landmarks were measured on 100 cadavers less than 24 h after death. Results and discussions: Of the 12 anatomical landmarks, only one appears to manifest statistically significant differences between sexes, and seven landmarks appear to vary significantly among the weight categories. Additionally, some evidence of interpopulation differences in the average soft tissue thicknesses were observed by the comparison of these data with similar studies from Caucasian groups with different geographic origin. Additionally, correlations between facial landmarks were examined, overall and between sexes.

Identifying skeletal remains has been and will remain a challenge for forensic doctors and forensic anthropologists, especially in disasters with multiple victims or skeletal remains in an advanced stage of decomposition. This study proposes a machine learning method to determine gender starting from morphometric analysis of L1-L5 lumbar vertebrae in a modern Romanian population. The purpose of the present study was to observe whether by using the ML method there is a good predictability of gender in forensic identification based on parameters obtained from the metric analysis of the lumbar spine specific to the Romanian population. This paper offers two models of ML, RF and XGB, each with its own characteristics, and presenting different performance, random forest having the best. For both, we used two metrics (accuracy and roc_auc), the latter being the most used to highlight model performance. The L1-L5 lumbar vertebrae exhibit sexual dimorphism and can be used in gender estimation. Machine learning is more accurate in determining gender than discriminatory function analysis.

Fracture treatment has experienced a fascinating evolution in the last years. The aim of this chapter is to reveal some clinical and biomechanical studies regarding innovative implants. After a short introduction (1), we intend to present our results regarding (2) dynamic condylar screw versus condylar blade plate in complex supracondylar femoral fractures; (3) biomechanical analysis of four types of implants in humeral fractures; (4) clinical and experimental studies for optimal stabilization of trochanteric fractures: the gliding nail; (5) intramedullary XS nail for pilon and ankle fractures: design, biomechanics, and clinical results; (6) the XS nail for the treatment of patella and olecranon fractures; and (7) plates with polyaxial stability for fractures of distal radius and proximal humerus. In conclusion, the authors highlight the advantages of these innovative implants in difficult trauma cases.

Introduction: The global spread of the SARS-CoV-2 pandemic led to a serious health, social and economic global crisis. This pandemic was and remains the most important health emergency worldwide, for which all professionals have been called to provide diagnosis and treatment support. Despite early concerns about safety, forensic medicine has contributed to a better understanding of the pathological mechanisms involved. Objective: This study aims to describe and analyze the postmortem pathological findings in confirmed SARS-CoV-2 cases, emphasizing the contribution of forensic autopsies to elucidating the mechanisms of death and associated comorbidities. Methods: A retrospective study was conducted on 279 autopsies between 2020 and 2022. Demographic, clinical, and pathological data were collected and statistically analyzed. Results: Following the descriptive analysis of the cases included in the study, as well as the analysis of the relevant scientific literature, the major impact of the COVID-19 pandemic was highlighted in terms of the death mechanisms involved, occurring consequences and induced changes. Conclusions: Autopsies remain the essential tools for investigating COVID-19-related deaths. The findings confirm that SARS-CoV-2 primarily affects the pulmonary and cardiovascular systems. However, the overlap between “death from” and “death with” COVID-19 highlights the need for standardized postmortem diagnostic criteria and comprehensive clinical–pathological correlation.