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YURTSEVER, İSMAİL

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Author: Yurtsever, İsmail × Has files: true ×

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    PublicationOpen Access
    COVİD-19 PANDEMİSİNDE Hastalık/Hastane Yönetimi-COVID-19 PANDEMİSİNDE RADYOLOJİK TANI YÖNTEMLERİ
    (2021-10-01T00:00:00Z) Yurtsever, İsmail; YURTSEVER, İSMAİL
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    PublicationOpen Access
    Machine Learning-Based Surgical Planning for Neurosurgery: Artificial Intelligent Approaches to the Cranium
    (2022-04-01T00:00:00Z) Dündar, Tolga Turan; Yurtsever, İsmail; Kurt Pehlivanoğlu, Meltem; Yıldız, Uğur; Eker, Ayşegül; Demir, Mehmet Ali; Mutluer, Ahmet Serdar; Tektaş, Recep; Kazan, Mevlude Sila; Kitiş, Serkan; Gokoglu, Abdulkerim; Doğan, İhsan; Duru, Nevcihan; DÜNDAR, TOLGA TURAN; YURTSEVER, İSMAİL; KİTİŞ, SERKAN
    Objectives: Artificial intelligence (AI) applications in neurosurgery have an increasing momentum as well as the growing number of implementations in the medical literature. In recent years, AI research define a link between neuroscience and AI. It is a connection between knowing and understanding the brain and how to simulate the brain. The machine learning algorithms, as a subset of AI, are able to learn with experiences, perform big data analysis, and fulfill human-like tasks. Intracranial surgical approaches that have been defined, disciplined, and developed in the last century have become more effective with technological developments. We aimed to define individual-safe, intracranial approaches by introducing functional anatomical structures and pathological areas to artificial intelligence. Methods: Preoperative MR images of patients with deeply located brain tumors were used for planning. Intracranial arteries, veins, and neural tracts are listed and numbered. Voxel values of these selected regions in cranial MR sequences were extracted and labeled. Tumor tissue was segmented as the target. Q-learning algorithm which is a model-free reinforcement learning algorithm was run on labeled voxel values (on optimal paths extracted from the new heuristic-based path planning algorithm), then the algorithm was assigned to list the cortico-tumoral pathways that aim to remove the maximum tumor tissue and in the meantime that functional anatomical tissues will be least affected. Results: The most suitable cranial entry areas were found with the artificial intelligence algorithm. Cortico-tumoral pathways were revealed using Q-learning from these optimal points. Conclusions: AI will make a significant contribution to the positive outcomes as its use in both preoperative surgical planning and intraoperative technique equipment assisted neurosurgery, its use increased
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    PublicationOpen Access
    Usefulness of oxidative stress marker evaluation at admission to the intensive care unit in patients with COVID-19
    (2021-07-01T00:00:00Z) Daşkaya, Hayrettin; Yılmaz, Sinan; Uysal, Harun; Sümbül, Bilge; Karaaslan, Kazım; DAŞKAYA, HAYRETTİN; YILMAZ, SİNAN; UYSAL, HARUN; ÇALIM, MUHITTIN; SÜMBÜL, BİLGE; YURTSEVER, İSMAİL; KARAASLAN, KAZıM
    Objective:Two critical processes in the coronavirus disease 2019 (COVID-19) pandemic involve assessing patients- intensive care needs and predicting disease progression during patients- intensive care unit (ICU) stay. We aimed to evaluate oxidative stress marker status at ICU admission and ICU discharge status in patients with COVID-19.Methods:We included patients in a tertiary referral center ICU during June-December 2020. Scores of Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA), and clinical severity, radiologic scores, and healthy discharge status were noted. We collected peripheral blood samples at ICU admission to evaluate total antioxidants, total oxidants, catalase, and myeloperoxidase levels.Results:Thirty-one (24 male, 7 female) patients were included. At ICU admission, patients- mean APACHE II score at ICU admission was 17.61 ± 8.9; the mean SOFA score was 6.29 ± 3.16. There was no significant relationship between clinical severity and oxidative stress (OS) markers nor between radiological imaging and COVID-19 data classification and OS levels. Differences in OS levels between patients with healthy and exitus discharge status were not significant.Conclusions:We found no significant relationship between oxidative stress marker status in patients with COVID-19 at ICU admission and patients- ICU discharge status.