Sex estimation from sacrum and coccyx with discriminant analyses and neural networks in an equally distributed population by age and sex

Sex estimation from the hyoid bone measurements in an adult Eastern Turkish population using 3D CT images, discriminant function analysis, support vector machines, and artificial neural networks☆

The hyoid bone is one of the bones in the human body that shows sexual dimorphism. The anthropological and anthropometric characteristics that determine sexual dimorphism are influenced by demographic differences. The aim of this study was to investigate the rate of sexual dimorphism of the hyoid bone in the adult Eastern Turkish population from the examination of the 3D computed tomography images of 240 patients, using discriminant function analysis (DFA), support vector machines (SVM), and artificial neural networks (ANN). These evaluations were based on eight hyoid measurements that have been frequently used in previous CT studies. The results showed that all eight measurements were higher in males than in females (p = 0.000). It was determined that sex could be estimated accurately at up to 93.3 % using DFA, 93.8 % using SVM and 95.4 % using ANN. The maximum accuracy rate achieved to 94.2 % in males using SVM, and 95.8 % in females using ANN. These high rates of sexual dimorphism found using DFA, SVM, and ANN in this study indicate that characteristics of the hyoid bone can be utilized to determine sex in the Eastern Turkish population.

Use of dimensions in posterior dentition for sex estimation in forensic contexts: A systematic review and meta-analysis

OBJECTIVE

This study assessed the validity of dimensions in posterior dentition for sex estimation in forensic contexts.

DESIGN

A systematic review was established following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). After assessing the risk of bias and methodological quality with the QUADAS-2 system, the data were subjected to statistical tests for a meta-analysis of diagnostic accuracy and I2 to verify the heterogeneity.

RESULTS

The search resulted in 15 studies that underwent qualitative testing, all were selected for quantitative analysis. The papers included: the mesiodistal of the upper first molar, lower first molar, and upper second molar, and the buccolingual of the upper first molar and upper second molar. The results showed that sensitivity and specificity rates were lower with the mesiodistal diameter, with rates of 0.577 for the lower first molar, 0.674 for the upper first molar, and 0.698 for the upper second molar, while the rates were higher with the buccolingual diameter, with 0.724 for the upper first molar, and 0.743 for the upper second molar. The power to estimate sex is greater for males than for females. High heterogeneity was detected among the studies of almost all dimensions, except sensibility for the lower first molar and specificity for the upper second molar.

CONCLUSIONS

None of the dimensions reached an accuracy of ≥80%, however, so they are not a reliable method for sex estimation in forensic practice.

The promise and limitations of artificial intelligence in musculoskeletal imaging

With the recent developments in deep learning and the rapid growth of convolutional neural networks, artificial intelligence has shown promise as a tool that can transform several aspects of the musculoskeletal imaging cycle. Its applications can involve both interpretive and non-interpretive tasks such as the ordering of imaging, scheduling, protocoling, image acquisition, report generation and communication of findings. However, artificial intelligence tools still face a number of challenges that can hinder effective implementation into clinical practice. The purpose of this review is to explore both the successes and limitations of artificial intelligence applications throughout the muscuskeletal imaging cycle and to highlight how these applications can help enhance the service radiologists deliver to their patients, resulting in increased efficiency as well as improved patient and provider satisfaction.

Sex estimation using foramen magnum measurements, discriminant analyses and artificial neural networks on an eastern Turkish population sample

BACKGROUND

Although many studies have been conducted using the foramen magnum for sex estimation, recent findings have indicated that the discriminant and regression models obtained from the foramen magnum may not be reliable. Artificial Neural Networks, was used as a classification technique in sex estimation studies on some other bones, did not used in sex estimation studies on the foramen magnum until now. The aim of this study was sex estimation on an Eastern Turkish population sample using foramen magnum measurements, discriminant analyses and Artificial Neural Networks.

METHODOLOGY

The study was performed on the CT images of a total of 720 cases, comprising 360 males and 360 females. For sex estimation, discriminant analysis and Artificial Neural Networks were used.

RESULTS

The accuracy rate was 86.7% with discriminant analysis and when sex estimation accuracy was determined according to cases with posterior probabilities above 95%, the accuracy ranged from 0% to 33.3%. With the use of the discriminant formulas of 2 other studies, obtained from different Turkish samples, sex could be determined at a rate of 84.6%. Some formulas were found to be unsuccessful in sex estimation. Sex estimation accuracy of 88.2% was achieved with Artificial Neural Networks.

CONCLUSION

In this study, it was found that sex could be determined to some extent with discriminant formulas from other samples from the same population, although some formulas were unsuccessful. With the use of image processing techniques and machine learning algorithms, better results can be obtained in sex estimation.

Sex classification using the human sacrum: Geometric morphometrics versus conventional approaches

The human pelvis shows marked sexual dimorphism that stems from the conflicting selective pressures of bipedal locomotion and parturition. The sacrum is thought to reflect this dimorphism as it makes up a significant portion of the pelvic girdle. However, reported sexual classification accuracies vary considerably depending on the method and reference sample (54%-98%). We aim to explore this inconsistency by quantifying sexual dimorphism and sex classification accuracies in a geographically heterogeneous sample by comparing 3D geometric morphometrics with the more commonly employed linear metric and qualitative assessments. Our sample included 164 modern humans from Africa, Europe, Asia, and America. The geometric morphometric analysis was based on 44 landmarks and 56 semilandmarks. Linear dimensions included sacral width, corpus depth and width, and the corresponding indices. The qualitative inspection relied on traditional macroscopic features such as proportions between the corpus of the first sacral vertebrae and the alae, and sagittal and coronal curvature of the sacrum. Classification accuracy was determined using linear discriminant function analysis for the entire sample and for the largest subsamples (i.e., Europeans and Africans). Male and female sacral shapes extensively overlapped in the geometric morphometric investigation, leading to a classification accuracy of 72%. Anteroposterior corpus depth was the most powerful discriminating linear parameter (83%), followed by the corpus-area index (78%). Qualitative inspection yielded lower accuracies (64–76%). Classification accuracy was higher for the Central European subsample and diminished with increasing geographical heterogeneity of the subgroups. Although the sacrum forms an integral part of the birth canal, our results suggest that its sex-related variation is surprisingly low. Morphological variation thus seems to be driven also by other factors, including body size, and sacrum shape is therefore likely under stronger biomechanical rather than obstetric selection.

An exploration of sacral morphology using geometric morphometrics and three-dimensionally derived interlandmark distances

INTRODUCTION

Accurate sex estimation is an important component of a biological profile in forensic anthropology. The pelvis is widely accepted as the most dimorphic osseous structure, and thus, this dimorphism is also reflected by the sacrum.

AIM

This study aimed to explore sacral morphology and to derive a practically applicable discriminant function formula for sex estimation.

MATERIALS

A total of 20 three-dimensional landmarks were digitised on a sample of 200 sacra from a sample of South Africans of African descent (Black South Africans) with ages ranging between 20 and 90 years, equally distributed for sex.

METHODS

Geometric morphometric methods were used to analyse sacral morphology and sexual dimorphism as it captures size-independent shape variation and three-dimensional morphology.

RESULTS

Size-independent shape analysis revealed four sacral structures and metrics that account for most of its shape variation. When these were compared between the sexes, we found that sacral curvature pattern, rather than depth, differed between sexes and that males have greater anterior sacral heights. Females have larger alae relative to the body of S1. In addition, the anterior posterior breadth of the sacral canal is larger in males, as is the relative size and projection of the superior articular processes. Discriminant analyses of these data produced average accuracies of only 72.5%, but this improved to 84.5% when using novel interlandmark distances derived from the raw coordinate data.

CONCLUSION

Our results demonstrate that landmark-based techniques allow for a more nuanced understanding of structural variation. In addition, accuracies were achieved that surpass traditional metrics using an equal number of variables. These results contribute to our understanding of sacral dimorphism and will assist in forensic casework.

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are potent in image processing and recognition using deep learning to perform generative and descriptive tasks. Compared to its predecessor, the advantage of CNN is that it automatically detects the important features without any human supervision. 3D CNN is used to extract features in three dimensions where input is a 3D volume or a sequence of 2D pictures, e.g., slices in a cone-beam computer tomography scan (CBCT). The main aim was to bridge interdisciplinary cooperation between forensic medical experts and deep learning engineers, emphasizing activating clinical forensic experts in the field with possibly basic knowledge of advanced artificial intelligence techniques with interest in its implementation in their efforts to advance forensic research further. This paper introduces a novel workflow of 3D CNN analysis of full-head CBCT scans. Authors explore the current and design customized 3D CNN application methods for particular forensic research in five perspectives: (1) sex determination, (2) biological age estimation, (3) 3D cephalometric landmark annotation, (4) growth vectors prediction, (5) facial soft-tissue estimation from the skull and vice versa. In conclusion, 3D CNN application can be a watershed moment in forensic medicine, leading to unprecedented improvement of forensic analysis workflows based on 3D neural networks.

The analysis of sacrum and coccyx length measured with computerized tomography images depending on sex

Background

Sex estimation is vital in establishing an accurate biological profile from the human skeleton, as sex influences the analysis of other elements in both Physical and Forensic Anthropology and Legal Medicine. The present study was conducted to analyze the sex differences between the sacrum and coccyx length based on the measurements calculated with computed tomography (CT) images. One hundred case images (50 females, 50 males) who were between the ages of 25 and 50 and admitted by the emergency department between September 2018 and June 2019 and underwent CT were included in the study. Eighteen lengths, 4 curvature lengths, and 2 regions were measured in sagittal, coronal and transverse planes with orthogonal adjustment for three times.

Results

It was stated that the mean anterior and posterior sacral length, anterior and posterior sacrococcygeal length, anterior and posterior sacral curvature length, anterior coccygeal curvature length, sacral area, lengths of transverse lines 1, 2, 3 and 4, sacral first vertebra transverse and sagittal length measurements were longer in males when compared to females (p < 0.05). It was noted that the parameter with the highest discrimination value in the receiver operating characteristic (ROC) analysis was the sacral area (AUC = 0.88/Acc = 0.82). Based on Fisher’s linear discriminant analysis findings, the discrimination rate was 96% for males, 92% for females and the overall discrimination rate was 94%.

Conclusions

It was concluded that the fourteen parameters that were indicated as significant in the present study could be used in anthropology, Forensic Medicine and Anatomy to predict sex.

Sacral morphometrics for sex estimation of dead cases in Central Thailand

Sex estimation by various forensic anthropology approaches is a crucial factor for identification of human skeletal remains. However, inexpensive, uncomplicated and reliable methods are still required, especially in a remote crime scene and a high crime incidence area. Here, we examined 13 sacral parameters from 78 independent skeletons derived from deceases found in Central Thailand (male, n = 46; female, n = 32) using simple standard anthropometric techniques for sex allocation. Discriminant analysis exhibited that anterior-posterior diameter of S₁ vertebra corpus (APS) is the most accurate sacral parameter for sex determination in our study with 82.1% of correct discrimination rate. The accuracy could be improved up to 97.4% when additional three sacral variables including the length of sacrum measured from the medial anterior-superior sacral promontory to the medial anterior-inferior S₅ vertebra (ASL), alar index (ALI), and the maximum anterior breadth of sacrum measured across sacral alar (ABS) were computed together with APS. These encourage the use of sacral morphometrics for sex assessment of human sacrum remains in Central Thailand. However, further investigation with broadening sacral morphometric data across the country might provide a promising sex determination equation from a sacral skeleton for Thai population.

Forensic sex estimation using the vertebrae: an evaluation on two European populations

Sex estimation is one of the primary steps for constructing the biological profile of skeletal remains leading to their identification in the forensic context. While the pelvis is the most sex diagnostic bone, the cranium and other post-cranial elements have been extensively studied. Earlier research has also focused on the vertebral column with varying results regarding its sex classification accuracy as well as the underlying population specificity. The present study focuses on three easily identifiable vertebrae, namely T1, T12, and L1, and utilizes two modern European populations, a Greek and a Danish, to evaluate their forensic utility in sex identification. To this end, 865 vertebrae from 339 individuals have been analyzed for sexual dimorphism by further evaluating the effects of age-at-death and population affinity on its expression. Our results show that T1 is the best sex diagnostic vertebra for both populations reaching cross-validated accuracy of almost 90%, while age-at-death has limited effect on its sexual dimorphism. On the contrary, T12 and L1 produced varying results ranging from 75 to 83% accuracy with the Greek population exhibiting distinctively more pronounced sexual dimorphism. Additionally, age-at-death had significant effect on sexual dimorphism of T12 and L1 and especially in the Greek female and Danish male groups. Our results on inter-population comparison suggest that vertebral sex discriminant functions, and especially those utilizing multiple measurements, are highly population specific and optimally suitable only for their targeted population. An open-source software tool to facilitate classifying new cases based on our results is made freely available to forensic researchers.

Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report

Artificial intelligence (AI) will transform every step in the imaging value chain, including interpretive and noninterpretive components. Radiologists should familiarize themselves with AI developments to become leaders in their clinical implementation. This article explores the impact of AI through the entire imaging cycle of musculoskeletal radiology, from the placement of the requisition to the generation of the report, with an added Canadian perspective. Noninterpretive tasks which may be assisted by AI include the ordering of appropriate imaging tests, automatic exam protocoling, optimized scheduling, shorter magnetic resonance imaging acquisition time, computed tomography imaging with reduced artifact and radiation dose, and new methods of generation and utilization of radiology reports. Applications of AI for image interpretation consist of the determination of bone age, body composition measurements, screening for osteoporosis, identification of fractures, evaluation of segmental spine pathology, detection and temporal monitoring of osseous metastases, diagnosis of primary bone and soft tissue tumors, and grading of osteoarthritis.