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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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    Morphometry of the Middle Cerebral Arteries: A Radio-Anatomical Study Based on Computed Tomography Angiography Findings
    (Universidad de la Frontera, 2023) Çiftçi, R.; Toy, S.; Ulubaba, H.E.; Senol, D.; Çinarli, F.S.; Sigirci, A.
    Middle cerebral artery (MCA), which has the largest irrigation area of the arteries that feed the brain, is an important artery whose microanatomy should be well known because of its vascular variation. In pathologies which are known to affect the cerebrovascular system such as type 2 diabetes mellitus (T2DM) and hypertension, morphometric characteristics of MCA gain importance. The aim of this study is to compare the morphometric characteristics of M1 segment of MCA in T2DM and hypertensive patients with those of healthy control group by using computed tomographic angiography (CTA). The study was carried out with retrospective morphometric analysis of CTA images of 200 individuals between 40 and 65 years of age. The individuals were grouped in four as hypertensive patients (group 1), patients with T2DM (group 2), patients with hypertension and T2DM (group 3) and healthy control group (group 4). Length and diameter measurements of M1 segment were performed and recorded by using 3D CTA images. While statistically significant difference was found between bilateral M1 segment diameters of both women and men (p<0.05), no statistically significant difference was found between segment lengths (p>0.05). As a result of the post hoc analysis performed, it was concluded that right and left M1 segment diameter of group 1, group 2 and group 3 was found to be different from group 4 in both sexes (p<0.05). We believe that this study will both be a guide in radio-anatomic assessments to be performed and also increase microanatomic level of information in the surgical treatment of the artery by showing the morphometric changes that occur in M1 segment of MCA in T2DM diseases. © 2023, Universidad de la Frontera. All rights reserved.
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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)