Gender Prediction with the Parameters Obtained from Pelvis Computed Tomography Images and Machine Learning Algorithms

Yusuf Seçgin · Zülal Öner · Muhammed Kamil Turan · Serkan Öner

18 citations0/0 files storedJournal of Anatomical Society of India

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Forensic Anthropology and Bioarchaeology Studies Image Processing and 3D Reconstruction Medical Imaging and Analysis

Abstract

Introduction: In the skeletal system, the most dimorphic bones employed for postmortem gender prediction include the bones in the pelvic skeleton. Bone measurements are usually conducted with cadaver bones. Computed tomography (CT) is an increasingly popular method due to its ease of use, reconstruction opportunities, and lower impact of age bias and provides a modern data source. Even when parameters obtained with different or same bones are missing, machine learning (ML) algorithms allow the use of statistical methods to predict gender. This study was carried out in order to obtain high accuracy in estimating gender with the pelvis skeleton by integrating ML algorithms, which are used extensively in the field of engineering, in the field of health. Material and Methods: In the present study, pelvic CT images of 300 healthy individuals (150 females, 150 males) between the ages of 25 and 50 (the mean female age = 40, the mean male age = 37) were transformed into orthogonal images, and landmarks were placed on promontory, iliac crest, sacroiliac joint, anterior superior iliac spine, anterior inferior iliac spine, terminal line, obturator foramen, greater trochanter, lesser trochanter, femoral head, femoral neck, body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and coordinates of these regions were obtained. Four groups were formed based on various angle and length combinations obtained from these coordinates. These four groups were analyzed with ML algorithms such as Logistic Regression, Linear Discriminant Analysis (LDA), Random Forest, Extra Trees Classifier, and ADA Boost Classifier. Results: In the analysis, it was determined that the highest accuracy was 0.96 (sensitivity 0.95, specificity 0.97, Matthew's Correlation Coefficient 0.93) with LDA. Discussion and Conclusion: The use of length and angle measurements obtained from the pelvis showed that the LDA model was effective in estimating gender.

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References

2011Sex Estimation in Forensic Anthropology: Skull Versus Postcranial Elements 2014Sex estimation from the tarsal bones in a Portuguese sample: a machine learning approach 2014Statistical sex determination from craniometrics: Comparison of linear discriminant analysis, logistic regression, and support vector machines 2017Sex determination from the femur in Portuguese populations with classical and machine-learning classifiers 2019CADOES: An interactive machine-learning approach for sex estimation with the pelvis 2014Morphometric sex estimation from 3D computed tomography os coxae model and its validation in skeletal remains 2017An Investigation into the Relationship between Human Cranial and Pelvic Sexual Dimorphism 2019The accuracy of 3D virtual bone models of the pelvis for morphological sex estimation

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