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Year : 2022  |  Volume : 71  |  Issue : 3  |  Page : 204-209

Gender prediction with the parameters obtained from pelvis computed tomography images and machine learning algorithms

1 Department of Anatomy, Faculty of Medicine, Karabük University, Karabük, Turkey
2 Department of Anatomy, Faculty of Medicine, İzmir Bakırçay University, İzmir, Turkey
3 Department of Medical Biology, Faculty of Medicine, Karabük University, Karabük, Turkey
4 Department of Radiology, Faculty of Medicine, İzmir Bakırçay University, İzmir, Turkey

Correspondence Address:
Dr. Zulal Oner
Department of Anatomy, Faculty of Medicine, İzmir Bakırçay University, İzmir
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jasi.jasi_280_20

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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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