Yazar "Akbulut, Sami" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    Artificial Intelligence and Decision Support Applications in Liver Hydatid Disease: Detection, Classification, and Complication Prediction
    (Springer Science+Business Media, 2025) Karaduman, Mucahit; Yildirim, Muhammed; Akbulut, Sami
    Hydatid disease, caused by Echinococcus spp., is a parasitic infection commonly observed in endemic regions such as the Middle East, South America, and Central Asia. Imaging modalities like ultrasonography, computed tomography, and magnetic resonance imaging play a crucial role in diagnosing and managing this disease. However, these techniques have some limitations, particularly concerning diagnostic accuracy, operator dependency, and the inability to predict complications in advance. This chapter comprehensively addresses the use of artificial intelligence (AI) and clinical decision support systems in managing liver hydatid disease within this context. Since the disease most commonly affects the liver, the chapter specifically focuses on liver hydatid disease. AI-based technologies are increasingly utilized to overcome these challenges and optimize diagnostic processes. Deep learning algorithms (e.g., CNN, U-Net) have demonstrated high accuracy in analyzing imaging data. These algorithms enable the automated staging of liver hydatid cysts and predict the risk of complications. Notably, the automation of staging systems accelerates clinical decision-making and reduces discrepancies among expert opinions. Furthermore, surgical clinical decision support systems and complication prediction models not only enhance diagnostic processes but also make treatment planning more reliable. Despite the promising potential of AI models, their widespread clinical adoption faces obstacles such as the lack of high-quality data sets and the challenge of making model decisions interpretable. Therefore, multicenter studies and model validations based on extensive data sets are essential for integrating AI more effectively into clinical practice. In conclusion, AI-powered clinical decision support systems hold significant potential for standardizing and expediting the diagnostic and therapeutic processes in liver hydatid disease. However, further research is necessary to ensure their seamless integration into clinical practice. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
  • Küçük Resim Yok
    Öğe
    Stigmatization of healthcare professionals during the COVID-19 pandemic: their psychosocial states and the factors affecting them
    (Kare Publ, 2023) Tekin, Cigdem; Karakas, Nese; Akbulut, Sami; Kurt, Harun; Bentli, Recep
    Objectives: It is assumed that healthcare professionals are directly or indirectly subjected to stigma during the COVID-19 pandemic, impacting their psychosocial health. This study aimed to evaluate the psychosocial status of healthcare professionals during the COVID-19 pandemic and examine the factors affecting their exposure to stigma. Methods: This cross-sectional study included all healthcare professionals (n=1132) working in primary and secondary healthcare institutions in Malatya Province. Descriptive questions were asked to measure the stigma experienced by healthcare professionals during the COVID-19 outbreak. The Zung Self-Rating Depression Scale and Insomnia Severity Index were used to evaluate psychosocial health status. Results: Of the participants, 68.7% stated that they were exposed to stigma because they are healthcare professionals. The findings indicated that 72.1% of those who felt stigmatized for being a healthcare professional suffered from mod-erate or severe depression, and 66.9% suffered from subthreshold or moderate insomnia. When their current health state was compared with that before the pandemic, 25.0% said that it became worse\much worse. Conclusion: The results of this study indicated that most participants had been exposed to stigmatization because they are healthcare professionals. The participants who were exposed to stigma were found to suffer more from de-pression and insomnia. When their current health state was compared with that before the pandemic, one of every four participants stated that it became worse/much worse.