TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/4
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Article Investigation of Glass Ceiling Syndrome Among Radiation Professionals: a Comparative Analysis(Dokuz Eylul Univ inst Health Sciences, 2025-05-31) Şişman, Gizem; Çilengiroğlu, Özgül Vupa; Alkan, TurkanBackground and Purpose: This study investigates the perception of the glass ceiling syndrome among radiology, nuclear medicine, and radiation oncology technicians in healthcare institutions in Turkey. Methods: A comparative approach was used to examine the prevalence and impact of the glass ceiling on female workers. Data was collected via questionnaires from 311 participants in Turkey, and analyzed using descriptive statistics, chi-square analysis, and independent sample tests. Results: The results indicate that 78.1% of the participants were women, 64% were medical imaging technicians and 65.3% were employed in private institutions. A significant difference was found in the total and subscale scores of the glass ceiling scale (excluding mentoring) based on gender (p<0.05). Conclusion: This study enhances understanding of gender dynamics among radiation workers and highlights the need for targeted interventions to address the glass ceiling syndrome. The findings provide key insights for promoting workforce equity and organizational development in healthcare institutions.Article Citation - WoS: 7Citation - Scopus: 10Investigation of the Role of Convolutional Neural Network Architectures in the Diagnosis of Glaucoma Using Color Fundus Photography(Turkish Ophthalmological Soc, 2022-06-29) Atalay, Eray; Ozalp, Onur; Devecioglu, Ozer Can; Erdogan, Hakika; İnce, Türker; Yildirim, NilgunObjectives: To evaluate the performance of convolutional neural network (CNN) architectures to distinguish eyes with glaucoma from normal eyes. Materials and Methods: A total of 9,950 fundus photographs of 5,388 patients from the database of Eskisehir Osmangazi University Faculty of Medicine Ophthalmology Clinic were labelled as glaucoma, glaucoma suspect, or normal by three different experienced ophthalmologists. The categorized fundus photographs were evaluated using a state-of-the-art two-dimensional CNN and compared with deep residual networks (ResNet) and very deep neural networks (VGG). The accuracy, sensitivity, and specificity of glaucoma detection with the different algorithms were evaluated using a dataset of 238 normal and 320 glaucomatous fundus photographs. For the detection of suspected glaucoma, ResNet-101 architectures were tested with a data set of 170 normal, 170 glaucoma, and 167 glaucoma-suspect fundus photographs. Results: Accuracy, sensitivity, and specificity in detecting glaucoma were 96.2%, 99.5%, and 93.7% with ResNet-50; 97.4 degrees A, 97.8%, and 97.1% with ResNet-101; 98.9%, 100%, and 98.1% with VGG-19, and 99.4%, 100%, and 99% with the 2D CNN, respectively. Accuracy, sensitivity, and specificity values in distinguishing glaucoma suspects from normal eyes were 62%, 68%, and 56% and those for differentiating glaucoma from suspected glaucoma were 92%, 81%, and 97%, respectively. While 55 photographs could be evaluated in 2 seconds with CNN, a clinician spent an average of 24.2 seconds to evaluate a single photograph. Conclusion: An appropriately designed and trained CNN was able to distinguish glaucoma with high accuracy even with a small number of fundus photographs. Conclusion: An appropriately designed and trained CNN was able to distinguish glaucoma with high accuracy even with a small number of fundus photographs.Article The Responses of Radiology Professionals To the Covid-19 Pandemic(Dokuz Eylul Univ Inst Health Sciences, 2022-09-29) Alkan, Turkan; Çilengiroğlu, Özgül VupaPurpose: This study aimed to investigate radiology professionals’ response to the impact of COVID-19 on professional practice. In addition, the fear and anxiety levels experienced by this workforce during the pandemic process were investigated. Methods: A quantitative cross-sectional study was conducted. The questionnaire covered information on demographic characteristics, the Coronavirus Overviews and Impacts, the Coronavirus Anxiety Scale (CAS), and the Fear of COVID-19 Scale. Logistic regression was used to model the relationship between \"CAS\" and \"Fear\" scores and variables. Data collected was analysed using the Statistical Package for Social Sciences (v.24). Results: A total of 290 responses were received, comprising 21.7% radiologists and 78.3% technicians. The key contributor factors to work-related stress were found to be the fear of COVID-19 infection, with 63.8%, the increase in workload, with 17.6% and inadequate personal protective equipment (PPE), with 11%. The percentages of anxiety were 75.6% for technician and 24.4% for radiologist. It was found that there was a significant association between \"CAS\" score and the gender variable (p=0.030<0.05), and similarly, between \"Fear\" score and gender (p-value=0.003) and age (p-value=0.080) variables. The women are 2.205 times more likely to be anxious than men (p=0.033) and 2.106 times more likely to be fear (p=0.003). Conclusion: Almost half of the participants reported adequate PPE availability during the study period. Despite this, most feared being infected with COVID-19. Therefore, it is important to provide timely and adequate personnel training, adequate availability of PPE and regular psychosocial support for radiology professinals, during future pandemics.