3D analysis of computed tomography (CT)–derived lumbar spine models for the estimation of sex

The footprint mismatch of cervical disc arthroplasty comes from degenerative factor besides ethnic factor

A mismatch in footprints of cervical total disc arthroplasty (CTDA) implants occasionally occurred in Asian population and it had been attributed solely to ethnic factor. Yet, cervical degeneration process may play a role. Our purpose was to compare the cervical vertebra morphometric data with and without degeneration. The study included patients with CT scans of cervical spine from our hospital between January, 2019, and September, 2021. The total cervical degenerative index (TCDI) of each patient were collected by adding CDI score for 5 disc-levels. Patients were categorized into normal (TCDI 0-5) and degeneration groups (TCDI 6-60). Various measurements of the C3-C7 vertebral body and endplate were taken. Forty-nine patients in the normal group and 55 in the degeneration group were included. No significant difference was noted in gender, BH, BW, or BMI except age and TCDI (p < .001). During degeneration, disproportional endplate size changes were observed, with an increment ratio of 12-20% in the anteroposterior and 5-17% in the mediolateral plane throughout C3-C7, while vertebral body height remained constant. In conclusion, degeneration process, besides ethnic factor, causes the endplate size and shape mismatch. This information can help spine surgeon choose appropriate implants in CTDA surgery.

Pediatric Solid-State 3D Models of Lumbar Vertebrae and Spine

Introduction While various 3D vertebral models have been utilized in numerous studies, there is a notable gap in the representation of pediatric lumbar vertebrae and spine. This study aimed to describe the changing shapes of lumbar vertebrae and spine with age and to develop precise 3D models. Materials and methods Solid-state 3D models of pediatric lumbar vertebrae and spine were created using SOLIDWORKS® Simulation software for five age groups: newborns, infants (ages 0-1), toddlers (ages 1-3), middle childhood (ages 4-7), and preadolescents (ages 8-12). Models were composed of components with varying biomechanical characteristics. Results Created 3D models replicate variations in the dimensions and configurations of vertebrae, taking into account osteometric analyses conducted on actual vertebral specimens. These models also include elements made of cartilage, representing various phases of vertebral growth during ontogeny. Additionally, through 3D parametric design, we developed comprehensive lumbar spine models, incorporating both the vertebrae and intervertebral disks. Conclusion Created pediatric solid-state vertebral 3D models can be utilized in developing virtual or augmented reality applications and for medical research. Users can interact with models, allowing virtual exploration and manipulation, enhancing learning experiences and facilitating a better understanding of spatial relationships. These solid-state 3D models allow finite element analysis and can be used for further research to calculate internal relative deformations and stress distribution under different conditions.

Machine Learning Models for Prediction of Sex Based on Lumbar Vertebral Morphometry

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.

Deep learning in sex estimation from a peripheral quantitative computed tomography scan of the fourth lumbar vertebra-a proof-of-concept study

Sex estimation is a key element in the analysis of unknown skeletal remains. The vertebrae display clear sex discrepancy and have proven accurate in conventional morphometric sex estimation. This proof-of-concept study aimed to investigate the possibility to develop a deep learning algorithm for sex estimation even from a single peripheral quantitative computed tomography (pQCT) slice of the fourth lumbar vertebra (L4). The study utilized a total of 117 vertebrae from the Terry Anatomical Collection. There were 58 male and 59 female cadavers, all of the white ethnicity, with the average age at death 49 years and a range of 24 to 77 years. A coronal pQCT scan was taken from the midway of the L4 corpus. Sex estimation was performed in a total of 19 neural network architectures implemented in the AIDeveloper software. Of the explored architectures, a LeNet5-based algorithm reached the highest accuracy of 86.4% in the test set. Sex-specific classification rates were 90.9% among males and 81.8% among females. This preliminary finding advances the field by encouraging and directing future research on artificial intelligence-based methods in sex estimation from individual skeletal traits such as the vertebrae. Combining quickly obtained imaging data with automated deep learning algorithms may establish a valuable pipeline for forensic anthropology and provide aid when combined with traditional methods.

Second and third lumbar vertebral parameters for prediction of sex, height, and age in the Iranian population

In this study, we aimed to assess the association between different parameters of the second and third lumbar vertebra with age, sex, and height in the Iranian population. A total of 14 parameters of the L2 and L3 vertebra were measured from three-dimensional lumbar topography. The measured parameters included vertebral length, foramen diameter, foramen width, endplate depth, endplate width, spinal process height, spinal process length, transverse process distance, the height of the vertebral body, articular process height inferior, articular process height superior, pedicle height, pedicle width, and maximum distance between articular processes. A total of 100 patients, including 46 males (46%) and 54 females (54%), were enrolled in this study. Our findings showed that most L2 and L3 parameters could differentiate males from females, with the area under the curve between 0.620 and 0.888. The majority of L2 and L3 parameters were positively associated with height in both males and females. Regarding age, there was a significant positive association between the spinal process length of L2 and vertebral length, spinal process height, and spinal process length of L3 with age in males. Also, several parameters of L2 and L3 were associated with age in females. In conclusion, we demonstrated that the parameters of the second and third lumbar vertebra could be valuable in the determination of the age, height, and sex of the Iranian population. Our results could have practical implications in forensic anthropology in serious events like earthquakes.

Deep learning and morphometric approach for Sex determination of the lumbar vertebrae in a Thai population

Sex determination is a fundamental step in biological profile estimation from skeletal remains in forensic anthropology. This study proposes deep learning and morphometric technique to perform sex determination from lumbar vertebrae in a Thai population. A total of 1100 lumbar vertebrae (L1-L5) from 220 Thai individuals (110 males and 110 females) were obtained from the Forensic Osteology Research Center, Faculty of Medicine, Chiang Mai University, Thailand. In addition, two linear measurements of superior and inferior endplates from the digital caliper and image analysis were carried out for morphometric technique. Deep learning applied image classification to the superior and inferior endplates of the lumbar vertebral body. All lumbar vertebrae images are included in the dataset to increase the number of images per class. The accuracy determined the performance of each technique. The results showed the accuracies of 82.7%, 90.0%, and 92.5% for digital caliper, image analysis, and deep learning techniques, respectively. The lumbar vertebrae L1-L5 exhibit sexual dimorphism and can be used in sex estimation. Deep learning is more accurate in determining sex than the morphometric method. In addition, the subjectivity and errors in the measurement are decreased. Finally, this study presented an alternative approach to determining sex from lumbar vertebrae when the more traditionally used skeletal elements are incomplete or absent.

Sex estimation using the human vertebra: a systematic review

Background

The vertebral column has been used in forensic studies for its weight-bearing function and relative density. Sex estimation is one of the essential elements in an anthropological examination, as it may narrow down the possibility of a match by half. Hence, it is crucial to derive the population-specific reference data in each vertebra for sex estimation. This systematic review explored the most sexually dimorphic vertebra by using the conventional anthropometric analysis.

Main body

An electronic comprehensive search was conducted using databases such as Scopus, Web of Science (WOS) and EBSCO Medline for relevant studies between 2008 and 2020. The main inclusion criteria were studies in English, and studies on sex estimation by morphometric analysis of vertebra by CT scan or dry bone. Only studies related to human adult age and vertebra were analysed. Literature search identified 84 potentially relevant articles, in which 19 articles had fulfilled the inclusion criteria. This review included studies on the cervical, thoracic and lumbar vertebrae in different populations.

Conclusion

The vertebral spine has demonstrated significant sexual dimorphism with variable prediction accuracies, whereby the body of a vertebra was found to be sexually dimorphic. It was shown that high accuracy of sex classification was provided by the second cervical, twelfth thoracic and first lumbar vertebrae, especially when they were used in combination.

Virtual Morphometry of the First Lumbar Vertebrae for Estimation of Sex Using Computed Tomography Data in the Turkish Population

Introduction

It may be necessary to make sex estimation by examining other bones that have been obtained intact. Vertebrae, especially the thoracic and lumbar vertebrae, are among the best-preserved skeletal elements from the forensic and archaeological point of view. Therefore, lumbar vertebrae can become an important skeletal element for sex estimation. In our study, measurements were made on the CT images of the first lumbar vertebra, and the accuracy of sex estimation from the L1 vertebra was investigated in the Turkish population.

Materials and methods

Three dimensional (3D) models of the L1 vertebra were created from CT images of 241 (121 females and 120 males) individuals. Twenty-two linear measurements were taken in lateral, anterior, and superior views of 3D models of the L1 vertebra. Univariate and multivariate discriminant function analyzes were applied to the measured parameters to determine predictive rates of sex. Intra- and interobserver errors were calculated.

Results

 All linear measurements were higher in males than females. All parameters excluding SCD (Spinal canal depth), PLu (Upper pedicle length), PLI (Lower pedicle length), AHi (Inferior articular process height), and SPH (Spinous process height) showed statistically significant differences between sex. The highest rate of 70.5% was obtained for the EPWu (Upper end-plate width) and EPWl (Lower end-plate width) parameters. When all variables of L1 vertebra were included in the stepwise discriminant analysis, correct prediction rates were determined as 72.6%.

Conclusion

Our study is the first study in which L1 vertebrae are examined with the purpose of sex estimation in the Turkish population and we think that our data will be an important reference for sex estimation from the L1 vertebra in the Turkish 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.

Morphometric research and sex estimation of lumbar vertebrae in a contemporary Spanish population

In the identification and reconstruction of the biological profile of skeletal remains in physical and forensic anthropology, sex estimation is a fundamental step, and is essential to achieve methods that allow the highest allocation accuracy. This study proposes a method to estimate the sex of skeletal remains using lumbar vertebrae. A total of 33 linear measurements for each lumbar vertebra were taken in 94 identified, contemporary, and adult skeletal remains (46 male and 48 female) from the cemetery of San José in Granada. The sexual dimorphism of each variable was analyzed, an intra- and interobserver error analysis was developed to assess the variables with best concordance, and the discriminant equations were obtained through a binary logistic regression analysis. All lumbar vertebrae show statistically significant sexual dimorphism with higher values in males than in females. Equations with an allocation accuracy of between 80% and 94.5% were obtained, showing that the lumbar vertebrae are a useful alternative for sex estimation when other skeletal elements are not well-preserved or available. The differences of biogeographic history between populations could translate into morphometric differences, which means that it is not advisable to use discriminant equations in a universal way. In spite of the high allocation accuracy of the equations developed, they should be applied in adults from populations similar to the present study to obtain reliable results of sexual estimation, until its validation in other populations.