PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://gcris.yasar.edu.tr/handle/123456789/11288

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Citation - WoS: 4
Citation - Scopus: 3
Deep learning-based classification of parotid gland tumors: integrating dynamic contrast-enhanced MRI for enhanced diagnostic accuracy
(BMC, 2025) Kazim Ayberk Sinci; Ilker Ozgur Koska; Yusuf Kenan Cetinoglu; Nezahat Erdogan; Ali Murat Koc; Nuket Ozkavruk Eliyatkin; Cagan Koska; Barkan Candan; Erdogan, Nezahat; Sinci, Kazim Ayberk; Koska, Ilker Ozgur; Koc, Ali Murat; Cetinoglu, Yusuf Kenan; Eliyatkin, Nuket Ozkavruk; Candan, Barkan
BackgroundTo evaluate the performance of deep learning models in classifying parotid gland tumors using T2-weighted diffusion-weighted and contrast-enhanced T1-weighted MR images along with DCE data derived from time-intensity curves.MethodsIn this retrospective single-center study including a total of 164 participants 124 patients with surgically confirmed parotid gland tumors and 40 individuals with normal parotid glands underwent multiparametric MRI including DCE sequences. Data partitions were performed at the patient level (80% training 10% validation 10% testing). Two deep learning architectures (MobileNetV2 and EfficientNetB0) as well as a combined approach integrating predictions from both models were fine-tuned using transfer learning to classify (i) normal versus tumor (Task 1) (ii) benign versus malignant tumors (Task 2) and (iii) benign subtypes (Warthin tumor vs. pleomorphic adenoma) (Task 3). For Tasks 2 and 3 DCE-derived metrics were integrated via a support vector machine. Classification performance was assessed using accuracy precision recall and F1-score with 95% confidence intervals derived via bootstrap resampling.ResultsIn Task 1 EfficientNetB0 achieved the highest accuracy (85%). In Task 2 the combined approach reached an accuracy of 65% while adding DCE data significantly improved performance with MobileNetV2 achieving an accuracy of 96%. In Task 3 EfficientNetB0 demonstrated the highest accuracy without DCE data (75%) while including DCE data boosted the combined approach to an accuracy of 89%.ConclusionsAdding DCE-MRI data to deep learning models substantially enhances parotid gland tumor classification accuracy highlighting the value of functional imaging biomarkers in improving noninvasive diagnostic workflows.
Citation - WoS: 26
Citation - Scopus: 27
The impact of economic and social factors on the prevalence of hepatitis B in Turkey
(BMC, 2018) Selma Tosun; Olgu Aygun; Hulya Ozkan Ozdemir; Elif Korkmaz; Durmus Ozdemir; Ozdemir, Hulya Ozkan; Aygun, Olgu; Ozdemir, Durmus; Tosun, Selma; Korkmaz, Elif
Background: Viral Hepatitis is one of the major global health problems affecting millions of people every year. Limited information is available on the impact of social and economic factors on the prevalence of Hepatitis B virus (HBV) in Turkey. This study contrary to other studies in the literature was undertaken with the aim of examining the Majority of the excluded data come from the volunteers. Methods: There are medical and the social-economic factors affecting the prevalence of HBV. This research while taking medical factors as control variables clarify the social and economic factors affecting the prevalence of HBV by utilising clinical data with the use of the Binary Probit Model (BPM). The BPM estimation is a powerful tool to determine not only the factors but explain also the exact impacts of each factor. Results: The estimations of the BPM shows that economic and social variables such as age gender migration education awareness social welfare occupation are very important factors for determining HBV prevalence. Compared to the youngest population the 46 to 66+ age group has a higher prevalence of HBV. The male respondents were 5% more likely to develop HBV compared to females. When region-specific differences are taken into account migrating from the poorest parts of the country such as the eastern and south-eastern regions of Turkey are approximately 16% more likely to be infected. The welfare indicators such as a higher number of rooms in the respondent's house or flat decreases the probability of having HBV and relatively higher income groups are less likely to develop HBV compared to labourers. The Self-employed/Business owner/Public sector worker category are approximately 10% less likely to develop HBV. When people are aware of the methods of prevention of HBV they are 6% less likely to be infected. Previous HBV infection history increases the probability of having HBV again B by 17%. Conclusions: These findings strongly suggest that the impact of social and economic factors on the prevalence of HBV is vital. Any improvements in these factors are likely to reduce prevalence of HBV.
Citation - WoS: 3
Citation - Scopus: 4
The impact of socioeconomic factors on the healthcare costs of people living with HIV in Turkey
(BMC, 2020) Hulya Ozkan Ozdemir; Selma Tosun; Fatma Nur Karaman Kabadurmus; Durmus Ozdemir; Özdemir, Hülya Özkan; Özdemir, Durmuş; Tosun, Selma; Kabadurmuş, Fatma Nur Karaman
BackgroundThis study addresses an important field within HIV research the impact of socioeconomic factors on the healthcare costs of people living with HIV/AIDS (PLHIV). We aimed to understand how different socioeconomic factors could create diverse healthcare costs for PLHIV in Turkey.MethodsData were collected between January 2017 and December 2017. HIV-positive people attending the clinic who had been referred to the national ART programme from January 1992 until December 2017 were surveyed. The questionnaire collected socioeconomic data. The cost data for the same patients was taken from the electronic database Probel Hospital Information Management System (PHIMS) for the same period. The PHIMS data include costs for medication (highly active antiretroviral therapy or HAART) laboratory pathology radiology polyclinic examination and consultation hospitalisation surgery and intervention blood and blood products supplies and other costs. Data were analysed using STATA 14.2 to estimate the generalised linear model (GLM).ResultsThe findings of our GLM indicate that age gender marital and parental status time since diagnosis employment wealth status illicit drug use and CD4 cell count are the factors significantly related to the healthcare cost of patients. We found that compared with people who have AIDS (CD4 cells <200 cells/mm(3)) people who have a normal range of CD4 cells ( 500 cells/mm(3)) have $1046 less in expenditures on average. Compared to younger people (19-39years) older people (>= 55) have $1934 higher expenditures on average. Costs are $644 higher on average for married people and $401 higher on average for people who have children. Healthcare costs are $518 and $651 higher on average for patients who are addicted to drugs and who use psychiatric drug(s) respectively. Compared to people who were recently diagnosed with HIV people who were diagnosed >= 10years ago have $743 lower expenditures on average.ConclusionOur results suggest that in addition to immunological status socioeconomic factors play a substantial role in the healthcare costs of PLHIV. The key factors influencing the healthcare costs of PLHIV are also critical for public policy makers healthcare workers health ministries and employment community programs.

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