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    Gender Estimation from Morphometric Measurements of Mandibular Lingula by Using Machine Learning Algorithms and Artificial Neural Networks
    (Wolters Kluwer Medknow Publications, 2024) Senol, D.; Bodur, F.; Secgin, Y.; Sencan, D.; Duman, Sb; Oner, Z.
    Background:Sex determination from the bones is of great importance for forensic medicine and anthropology. The mandible is highly valued because it is the strongest, largest and most dimorphic bone in the skull.Aim:Our aim in this study is gender estimation with morphometric measurements taken from mandibular lingula, an important structure on the mandible, by using machine learning algorithms and artificial neural networks.Methods:Cone beam computed tomography images of the mandibular lingula were obtained by retrospective scanning from the Picture Archiving Communication Systems of the Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, & Idot;n & ouml;n & uuml; University. Images scanned in Digital Imaging and Communications in Medicine (DICOM) format were transferred to RadiAnt DICOM Viewer (Version: 2020.2). The images were converted to 3-D format by using the 3D Volume Rendering console of the program. Eight anthropometric parameters were measured bilaterally from these 3-D images based on the mandibular lingula.Results:The results of the machine learning algorithms analyzed showed that the highest accuracy was 0.88 with Random Forest and Gaussian Naive Bayes algorithm. Accuracy rates of other parameters ranged between 0.78 and 0.88.Conclusions:As a result of the study, it is thought that mandibular lingula-centered morphometric measurements can be used for gender determination as well as bones such as the pelvis and skull as they were found to be highly accurate. This study also provides information on the anatomical position of the lingula according to gender in Turkish society. The results can be important for oral-dental surgeons, anthropologists, and forensic experts.
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    Sex prediction with morphometric measurements of first and fifth metatarsal and phalanx obtained from X-ray images by using machine learning algorithms
    (Via Medica, 2023) Senol, D.; Bodur, F.; Secgin, Y.; Bakici, R. S.; Sahin, N. E.; Toy, S.; Oner, S.
    Background: The aim of this study is to predict sex with machine learning (ML) algorithms by making morphometric measurements on radiological images of the first and fifth metatarsal and phalanx bones.Materials and methods: In this study, radiologic images of 263 individuals (135 female, 128 male) between the ages of 27 and 60 were analysed retrospectively. The images in digital imaging and communications in medicine (DICOM) format were transferred to personal workstation Radiant DICOM Viewer programme. Length and width measurements of the first and fifth metatarsal and foot phalanx bones were performed on the transferred images. In addition, the ratios of the total length of the first proximal and distal phalanx and length of the first metatarsal and total length of fifth proximal, middle, and distal phalanx and maximum length of fifth metatarsal were calculated.Results: As a result of machine learning algorithms, highest accuracy, specificity, sensitivity, and Matthews correlation coefficient values were found as 0.85, 0.86, 0.85, and 0.71, respectively with decision tree algorithm. It was found that accu racy rates of other algorithms varied between 0.74 and 0.83. Conclusions: As a result of our study, it was found that sex estimation was made with high accuracy rate by using machine learning algorithms on X-ray images of the first and fifth metatarsal and foot phalanx. We think that in cases when pelvis, cranium and long bones are harmed and examination is difficult, bones of the first and fifth metatarsal and foot phalanx can be used for sex estimation. (Folia Morphol 2023; 82, 3: 704-711)