Image analysis of pubic bone for sex determination in a computed tomography sample

Sex estimation with the total area of the proximal femur: A densitometric approach

The estimation of sex is a central step to establish the biological profile of an anonymous skeletal individual. Imaging techniques, including bone densitometry, have been used to evaluate sex in remains incompletely skeletonized. In this paper, we present a technique for sex estimation using the total area (TA) of the proximal femur, a two-dimensional areal measurement determined through densitometry. TA was acquired from a training sample (112 females; 112 males) from the Coimbra Identified Skeletal Collection (University of Coimbra, Portugal). Logistic regression (LR), linear discriminant analysis (LDA), reduce error pruning trees (REPTree), and classification and regression trees (CART) were employed in order to obtain models that could predict sex in unidentified skeletal remains. Under cross-validation, the proposed models correctly estimated sex in 90.2-92.0% of cases (bias ranging from 1.8% to 4.5%). The models were evaluated in an independent test sample (30 females; 30 males) from the 21st Century Identified Skeletal Collection (University of Coimbra, Portugal), with a sex allocation accuracy ranging from 90.0% to 91.7% (bias from 3.3% to 10.0%). Overall, data mining classifiers, especially the REPTree, performed better than the traditional classifiers (LR and LDA), maximizing overall accuracy and minimizing bias. This study emphasizes the significant value of bone densitometry to estimate sex in cadaveric remains in diverse states of preservation and completeness, even human remains with soft tissues.

The geometrical precision of virtual bone models derived from clinical computed tomography data for forensic anthropology

Almost all European countries lack contemporary skeletal collections for the development and validation of forensic anthropological methods. Furthermore, legal, ethical and practical considerations hinder the development of skeletal collections. A virtual skeletal database derived from clinical computed tomography (CT) scans provides a potential solution. However, clinical CT scans are typically generated with varying settings. This study investigates the effects of image segmentation and varying imaging conditions on the precision of virtual modelled pelves. An adult human cadaver was scanned using varying imaging conditions, such as scanner type and standard patient scanning protocol, slice thickness and exposure level. The pelvis was segmented from the various CT images resulting in virtually modelled pelves. The precision of the virtual modelling was determined per polygon mesh point. The fraction of mesh points resulting in point-to-point distance variations of 2 mm or less (95% confidence interval (CI)) was reported. Colour mapping was used to visualise modelling variability. At almost all (>97%) locations across the pelvis, the point-to-point distance variation is less than 2 mm (CI = 95%). In >91% of the locations, the point-to-point distance variation was less than 1 mm (CI = 95%). This indicates that the geometric variability of the virtual pelvis as a result of segmentation and imaging conditions rarely exceeds the generally accepted linear error of 2 mm. Colour mapping shows that areas with large variability are predominantly joint surfaces. Therefore, results indicate that segmented bone elements from patient-derived CT scans are a sufficiently precise source for creating a virtual skeletal database.

Human identification through frontal sinus 3D superimposition: Pilot study with Cone Beam Computer Tomography

As a unique anatomical feature of the human body, the frontal sinus morphology has been used for identification of unknown bodies with many techniques, mostly using 2D postero-anterior X-rays. With the increase of the use of Cone-Beam Computer Tomography (CBCT), the availability of this exam as ante-mortem records should be considered. The purpose of this study is to establish a new technique for frontal sinus identification through direct superimposition of 3D volumetric models obtained from CBCT exam, by testing two distinct situations. First, a reproducibility test, where two observers independently rendered models of frontal sinus from a sample 20 CBCT exams and identified them on each other's list. In the second situation, one observer tested the protocol and established on different exams of three individual. Using the open source DICOM viewer InVesallius(®) for rendering, Mesh Lab(®,) for positioning the models and CloudCompare for volumetric comparison, both observers matched cases with 100% accuracy and the level of coincidence in a identification situation. The uniqueness of the frontal sinus topography is remarkable and through the described technique, can be used in forensic as an identification method whenever both the sinus structure and antemortem computer tomography is available.

Mandibular ramus length as an indicator of chronological age and sex

Age and sex estimation is crucial in forensic investigations, whether in legal situations that involve living people or to identify mortal remains. The aim of this study was to establish reference values in a Brazilian population to estimate age and sex by measuring the length of the mandibular ramus on lateral cephalometric radiographs, and to determine the probability that an individual being is 18 years or older, based on the results that were obtained. Two hundred and eighteen scanned lateral cephalograms of individuals between 6 and 20 years of age (101 males and 117 females) were measured with reference to mandibular ramus length (the distance between Condylion superior (Cs) and Gonion (Go)) using ImageJ 1.41 software (NIH, Bethesda, MA, USA). The results showed that sexual dimorphism was not observed until 16 years and, based on the ramus length measurements in this sample, it is possible to predict sex with an accuracy of only 54 %. There was a positive correlation between age and ramus length (r = 0.90; p < 0.001). From the linear regression analysis, one formula was derived; therefore, it was possible to calculate the individual's age, given his or her ramus length. The results showed that if an individual's ramus length is 7.0 cm or more, then there is an 81.25 % chance that the individual is 18 years old or older. In conclusion, the mandibular ramus length was not effective in discriminating sex. Mandibular length is strongly related to chronological age and can be used to predict whether an individual is 18 years or older with high degree of expected accuracy.