Development of a computer-assisted forensic radiographic identification method using the lateral cervical and lumbar spine

Technical Note: Individuals identified by radiographic comparison: A sample of demographics and the region of body used for identification in Clark County, Nevada, USA (2017-2020)

Radiographs regularly aid in identifications by comparing antemortem and postmortem images. The forensic community has widely accepted this method because radiographic images are low in cost, easily transferable, and generally believed to be uncomplicated to assess. However, there is a dearth of diverse, modern radiographic datasets as well as a deficiency in assessing population frequencies of nonmetric traits via radiographs. Further, radiographic comparisons, although typically required as a core competency of practicing forensic anthropologists, do not have standard training and education requirements. To understand the diversity and representation within an opportunistic dataset, we provide a case study of the demographics of decedents identified via radiographic comparison at the Clark County Office of the Coroner/Medical Examiner (CCOCME) in Las Vegas, NV, USA from 2017 to 2020. Additionally, we examined each region of the body used to make radiographic identifications. The sample was majority Caucasian and male, with a mean and median age at death of 64.9 years and 67.0 years, respectively. Our results indicated that this sample was not representative of the greater Clark County population. The most common body region used for comparisons was the chest, followed by the head and neck, pelvis, dentition, and the extremities. Thus, large generalizations made from these types of datasets must be implemented with caution due to the relative lack of diversity and representation. As well, current discussions regarding training and education of forensic anthropologists as they relate to radiographic comparisons are explored.

Fully automated radiologic identification focusing on the sternal bone

A crucial task in forensic investigations is the identification of unknown deceased. In general, secure identification methods rely on a comparison of ante mortem (AM) with post mortem (PM) data. However, available morphologic approaches are often dependent on the expertise and experience of the examiner, and often lack standardisation and statistical evidence. The objective of this study was therefore to overcome the current challenges via developing a fully automated radiologic identification (autoRADid) method based on the sternal bone. An anonymised AM data set consisting of 91 chest computed tomography (CT) scans, as well as an anonymised PM data set of 42 chest CT scans were included in this work. Out of the 91 available AM CT data sets, 42 AM scans corresponded to the 42 PM CT scans. For the fully automated identification analysis, a custom-made python pipeline was developed, which automatically registers AM data to the PM data in question using a two-step registration method. To evaluate the registration procedure and subsequent identification success, the image similarity measures Jaccard Coefficient, Dice Coefficient, and Mutual Information were computed. The highest value for each metric was retrieved in order to analyse the correspondence between AM and PM data. For all three similarity measures, 38 out of the 42 cases were matched correctly. This corresponds to an accuracy of 91.2%. The four unsuccessful cases incorporated surgical interventions taking place between the AM and the PM CT acquisition or poor CT scan quality preventing robust registration results. To conclude, the presented autoRADid method seems to be a promising fully automated tool for a reliable and facile identification of unknown deceased. A final pipeline combining all three similarity measures is open source and publicly available for efficient future identifications of unknown deceased.

Virtual anthropology - a brief review of the literature and history of computed tomography

Computed tomography (CT) has influenced numerous fields since its inception in the 1970s. The field of palaeoanthropology significantly benefited from this efficient and non-invasive medium in terms of the conservation, reconstruction and analysis of fossil human remains. Over the past decade, there has been a steady increase in the number of forensic anthropological studies incorporating virtual osteological analyses. Because of the increasing importance of these modern cross-sectional imaging techniques and the requirement for standardized parameters in forensic science, we deemed it important to outline the history and development of CT applications in these related academic areas. The present paper outlines the history of “virtual anthropology” and osteological multi-detector CT in the context of palaeoanthropology and forensic anthropology.

Evolution of Forensic Anthropological Methods of Identification

Forensic identification of human remains has long been a core contribution of forensic anthropologists to death investigations. The array and scientific robusticity of the identification methods employed by the anthropologist has evolved in the last several decades, and as with other nonidentification methods, anthropologists have embraced the progression toward the use of validated and statistically defensible methods for identification. This article presents an overview of the role that the forensic anthropologist plays in the identification of human remains and the evolution of anthropological methods of identification.

A Test of the DSP Sexing Method on CT Images from a Modern French Sample

The hip bone is considered to be one of the most reliable indicators in sex determination. The aim of this study was to test the reliability of the DSP method for the hip bone proposed by Murail et al. (Bull Mem Soc Anthropol Paris, 17, 2005, 167) on a sample from a present-day population in France (52 males and 54 females). Ten linear measurements were collected from three-dimensional models derived from computed tomography images (CTI). To quantify the proportions of correct sex determinations, a more rigorous posterior probability threshold of 0.95 was applied. Using all 10 measurements, 92.3% of males and 97.2% of females were sexed correctly. The percentage of undetermined specimens varied depending on the used combination of measurements; however, all sexes were assigned with a 100% accuracy. This study proves that DSP is an appropriate and reliable tool for sex determination, based on dimensions obtained from CTI.