Publications by authors named "B Ozmen"

This work explores the transformative potential of quantum computing (QC) in plastic and reconstructive surgery, highlighting its ability to enhance predictive modeling, surgical planning, and intraoperative guidance. By leveraging QC's superior computational power, clinicians have the capacity to improve personalized treatment plans and optimize surgical outcomes. Despite the challenges of cost, technical limitations, and ethical considerations, the integration of QC into clinical practice and research promises significant advancements in patient care and surgical innovation.

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Background: Anti-Müllerian hormone (AMH) is a widely used marker for estimating ovarian reserve, and it may predict response to ovarian stimulation. While AMH is considered a stable, cycle-independent marker, studies have shown it can exhibit significant fluctuations based on factors like age, reproductive stage, and menstrual cycle phase. The fluctuations in AMH levels can make it challenging to predict individual responses accurately, particularly when the AMH is not measured in the COS cycle.

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Background: The integration of robotic technology into surgical procedures has gained considerable attention for its promise to enhance a variety of clinical outcomes. Robotic deep inferior epigastric perforator (DIEP) flap harvest has emerged as a novel approach for autologous breast reconstruction. This systematic review aims to provide a comprehensive overview of the current techniques, outcomes, and complications of robotic DIEP flap surgery.

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Background: Lymphedema diagnosis relies on effective imaging of the lymphatic system. Indocyanine green (ICG) lymphography has become an essential diagnostic tool, but globally accepted protocols and objective analysis methods are lacking. In this study, we aimed to investigate artificial intelligence (AI), specifically convolutional neural networks, to categorize ICG lymphography images patterns into linear, reticular, splash, stardust, and diffuse.

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