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Öğe Determination of Gender By Machine Learning Algorithms, Through Using Craniocervical Junction Parameters and Dimensions of the Cervical Spinal Canal(2023) Şenol, Gamze Taşkin; Kürtül, Ibrahim; Ray, Abdullah; Ahmetoğlu, Gülçin; Seçgin, Yusuf; Öner, ZülalGender determination is the first step for biological identification. With the widespread use of machine learning algorithms (MLA) for diagnosis, the significance of applying them also in gender determination studies has become apparent. This study has therefore aimed at determining gender from the parameters obtained out of magnetic resonance images (MRI) of the cranio-cervical junction and cervical-spinal canal by using MLA. MRI of the craniocervical junction and cervical-spinal canal of 110 men and 110 women were included in this study. The 15 parameters were tested with Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) algorithms. Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), F1 score (F1), Matthews-correlation coefficient (Mcc) values were used as performance criteria. The Acc, Spe, Sen, F1, and Mcc were found to be 1.00 in the LR, LDA, QDA and RF algorithms. The ratios of the Acc, Spe, Sen, and F1 were 0.98, and of the Mcc was 0.96 in the DT algorithm. It was found that the ratio between the SHAP analyzer of the RF algorithm and the belt of the ratio between the arch of the atlas and the anterior-posterior distance of the dens (R3) parameter had a higher contribution to the estimation of gender compared to other parameters. It was concluded that the LDA, QDA, LR, DT and RF algorithms applied to the parameters acquired from the MRI of the craniocervical junction and cervical-spinal canal, could determine the gender with very high accuracy.Öğe Evaluation of the effect of COVID-19 on static balance in healthy young individuals(Bayrakol Medical Publisher, 2023) Şenol, Gamze Taşkın; Kürtül, İbrahim; Ray, Abdullah; Ahmetoğlu, GülçinAim: It is known that COVID-19 infection has various physiological effects. And, it also has negative effects on the balance. This study focused on evaluating the static balance of healthy individuals who either had or did not have a history of COVID-19.Material and Methods: The study included 30 individuals who were previously diagnosed with COVID-19 infection (positive PCR test), who recovered later on, and 30 individuals as a control group. After the dominant foot of both groups was determined, the flamingo balance test was used to evaluate static balance, and Dizziness Handicap Inventory (DHI) was applied to the group that had a COVID-19 infection history.Results: A significant difference was found between dominant foot balance and non-dominant foot balance in individuals who had COVID-19 and in the control group.Discussion: The severity of recent cases of COVID-19 disease that affect the balance system has risen significantly. This study showed that individuals with COVID-19 have problems with static balance compared to those without COVID-19. In our estimation, post-recovery rehabilitation programs for people who have had COVID-19 should include balancing exercises.Öğe Machine learning algorithms for sex classification by using variables of orbital structures: A computed tomography study(Soc Chilena Anatomia, 2024) Şenol, Gamze Taşkın; Kürtül, İbrahim; Ray, Abdullah; Ray, GülçinSince machine learning algorithms give more reliable results, they have been used in the field of health in recent years. The orbital variables give very successful results in classifying sex correctly. This research has focused on sex determinationusing certain variables obtained from the orbital images of the computerized tomography (CT) by using machine learning algorithms (ML).is In study th 12 variables determined on 600 orbital images of 300 individuals (150 men and 150 women) were tested with different ML. ree Decision (DT), t algorithms of ML were used for unsupervised learning. Statistical analyses of the variables were conducted with Minitab (R) 64-bit)21.2 ( program. ACC rate of NB, DT, KNN, and LR algorithms was found as % 83 while the ACC rate of LDA and RFC algorithms was determind as % 85. According to Shap analysis, the variable with the highest degree of effect was found as BOW. The study has thedetermined sex with high accuracy at the ratios of 0.83 and 0.85 through using the variables of the orbital CT images, and the related morphometricdata of the population under question was acquired, emphasizing the racial variation.Öğe Pediatrik dönemde beyin ventriküllerine ait parametrelerin yaşa ve cinsiyete bağlı değişimi(Bolu Abant İzzet Baysal Üniversitesi, 2022) Ray, Abdullah; Kürtül, İbrahimAraştırmanın amacı pediatrik dönem beyin ventriküllerinin yaşa ve cinsiyete bağlı değişimini incelemektir. Çalışma Bolu Abant İzzet Baysal Üniversitesi Picture Archiving and Communication System (PACS) arşiv görüntülerinden 0-18 yaş arası 200 sağlıklı bireyin MRI beyin görüntüleri incelenerek yapılmıştır. Çalışmada yer alan bireyler yaşa göre dört gruba ayrılmıştır. 1. grup 0-2 yaş arasındaki bireyler, 2. grup 3-6 yaş arasındaki bireyler, 3. grup 7-11 yaş arasındaki bireyler, 4. grup 12-18 yaş arasındaki bireyler olarak belirlenmiştir. Çalışmada üçüncü ventrikülün axial ölçümü (ÜVAÖ), cornu frontalis'in anterior genişliği (CFAG), cornu frontalis'in posterior genişliği (CFPG), cornu frontalis'in genişliği (CFG), cornu frontalis'in oblik çapı (CFOÇ), kafatasının maksimum transvers çapı (KMTÇ), kafatasının vertikal çapı (KVÇ), sağ cornu temporalis'in anterioposterior genişliği (SACTAG), sol cornu temporalis'in anterioposterior genişliği (SOCTAG), dördüncü ventrikülün anterioposterior genişliği (DVAG), dördüncü ventrikülün transvers genişliği (DVTG), evans indeksi (EI) ölçülmüştür. Çalışmanın sonucunda 1. grup için CFAG, CFPG, SACTAG, SOCTAG değişkenlerinin, 2. grup için CFAG, CFPG değişkenlerinin, 3. grup için KMTÇ değişkeninin, 4. grup için CFAG, CFPG, CFG, SACTAG, KMTÇ, KVÇ, DVTG değişkenlerinin cinsiyetler arası anlamlı bir farklılık bulunduğu tespit edilmiştir. İncelenen değişkenlerde CFG değişkeni yaş ile değişiklik göstermezken CFAG, DVAG, DVTG, CFOÇ, SOCTAG, KVÇ, KMTÇ değişkenlerinin boyutları yaş ile artış göstermekte EI ve ÜVAÖ değişkenleri yaş büyüdükçe azalmakta ve SACTAG değişkeninin boyutları de yaş ile birlikte önce artıp sonra azalmaktadır. Çalışmanın sonucu literatür ile paralellik göstermektedir.Öğe The relationship between body mass index and pronation response of the foot in healthy young individuals(2023) Taşkin, Rümeysa Gamze; Kürtül, Ibrahim; Ray, Abdullah; Ahmetoğlu, GülçinAim: This study aims to evaluate the relationship between body mass index and navicular drop in healthy young individuals by considering gender differences. In addition, it is to support the literature for a better understanding of the effect of the foot on the balance mechanisms and to contribute to the development of new approaches in addition to the existing treatment approaches. Methods: A hundred medical school students between the ages of 18-25 were included in our study. The participants’ age, height, and weight information were recorded, and body mass index (BMI) was calculated. The navicular drop test was performed to measure the pronation response of the foot. Results: The mean±SD values of the determined parameters in men and women were as follows respectively: Age: 20 and 20; Height (cm): 179±12.7 and 163±0.05, Weight (kg): 78.8±5.3 and 54.5; BMI (kg/m2): 24.4±3.5 and 20.2, right navicular tubercle height in a sitting position (SNTR): 43.7±4.8 and 38.7±5.1; right navicular tubercle height in a standing position (StNTR): 36.4±4.2 and 24.9±4.8; the navicular drop rate of the right foot (NDRR): 7.2±4.2 and 7.2±5.3; left navicular tubercle height in a sitting position (SNTL): 37.2±3.5 and 32.3±5.3; left navicular tubercle height in a standing position (StNTL): 30±4.7 and 31.5±5.2; the navicular drop rate of the left foot (NDRL): 7.2±4.7 and 7.4±4.6. Conclusion: As a result of our study, it is seen that an insignificant change in the rate of navicular drop as the body mass index increases in men and women.Öğe Sex determination by the machine learning algorithms through using morphometric measurements of the carpal, metacarpal, and phalangeal bones(Sociedad Chilena de Anatomía, 2023) Şenol, Gamze Taşkın; Kürtül, İbrahim; Ray, Abdullah; Ahmetoğlu, GülçinIn the study, it was aimed to predict sex from hand measurements using machine learning algorithms (MLA). Measurements were made on MR images of 60 men and 60 women. Determined parameters; hand length (HL), palm length (PL), hand width (HW), wrist width (EBG), metacarpal I length (MIL), metacarpal I width (MIW), metacarpal II length (MIIL), metacarpal II width (MIIW), metacarpal III length (MIIL), metacarpal III width (MIIIW), metacarpal IV length (MIVL), metacarpal IV width (MIVW), metacarpal V length (MVL), metacarpal V width (MVW), phalanx I length (PILL), measured as phalanx II length (PIIL), phalanx III length (PIIL), phalanx IV length (PIVL), phalanx V length (PVL). In addition, the hand index (HI) was calculated. Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), K-nearest neighbour (KNN) and Naive Bayes (NB) were used as MLAs. In the study, the KNN algorithm's Accuracy, SEN, F1 and Specificity ratios were determined as 88 %. In this study using MLA, it is understood that the highest accuracy belongs to the KNN algorithm. Except for the hand's MIIW, MIIIW, MIVW, MVW, HI variables, other variables were statistically significant in terms of sex difference.
