Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/11727/4809
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Item A Case of Transient Visual Field Defect following Administration of Pfizer-BioNTech COVID-19 Vaccine(2022) Sezenoz, Almila Sarigul; Gungor, Sirel Gur; Kibaroglu, Seda; https://orcid.org/0000-0002-7030-5454; https://orcid.org/0000-0001-6178-8362; https://orcid.org/0000-0002-3964-268X; 35404749; AAJ-4860-2021; AAD-5967-2021; AAJ-2956-2021Purpose To report a case of transient visual field (VF) defect after coronavirus disease-19 (COVID-19) vaccination. Case Report A 38-year-old Caucasian, otherwise healthy female patient, presented with a complaint of vision loss in the outer quadrant in her left eye after the second dose of Pfizer (R)-BioNTech (TM) COVID-19 vaccine. The Snellen visual acuity was 20/20 in both eyes. She did not have relative afferent pupillary defect nor disturbance of color vision. Her intraocular pressures, slit lamp and fundus examinations were normal. In the VF test, a temporal hemifield defect in the left eye and a nasal peripheral VF defect in the right eye were detected. Other imaging characteristics and neurological examination were normal. She was followed without any treatment. One week later, the patient was re-evaluated and complete resolution of the VF defect was observed. Conclusion Clinicians should be aware that patients can experience transient visual symptoms following COVID-19 vaccination.Item CNN-Based Severity Prediction Of Neurodegenerative Diseases Using Gait Data(2022) Erdas, Cagatay Berke; Sumer, Emre; Kibaroglu, Seda; https://orcid.org/0000-0003-3467-9923; 35111334; AGA-5711-2022Neurodegenerative diseases occur because of degeneration in brain cells but can manifest as impairment of motor functions. One of the side effects of this impairment is an abnormality in walking. With the development of sensor technologies and artificial intelligence applications in recent years, the disease severity of patients can be estimated using their gait data. In this way, decision support applications for grading the severity of the disease that the patient suffers in the clinic can be developed. Thus, patients can have treatment methods more suitable for the severity of the disease. The presented research proposes a deep learning-based approach using gait data represented by a Quick Response code to develop an effective and reliable disease severity grading system for neurodegenerative diseases such as amyotrophic lateral sclerosis, Huntington's disease, and Parkinson's disease. The two-dimensional Quick Response data set was created by converting each one-dimensional gait data of the subjects with a novel representation approach to a Quick Response code. This data set was regressed with the convolutional neural network deep learning method, and a solution was sought for the problem of grading disease severity. Further, to demonstrate the success of the results obtained with the novel approach, native machine learning approaches such as Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and K-Nearest Neighbours, and ensemble machine learning methods, such as voting and stacking, were applied on one-dimensional data. Finally, the results obtained on the prediction of disease severity by testing one-dimensional gait data with a convolutional neural network architecture that operates on one-dimensional data were included. The results showed that, in most cases, the two-dimensional convolutional neural network approach performed the best among all methods.Item A novel electroencephalography based approach for Alzheimer's disease and mild cognitive impairment detection(2021) Oltu, Burcu; Aksahin, Mehmet Feyzi; Kibaroglu, Seda; 0000-0002-3964-268X; AAJ-2956-2021Background and objective: Alzheimer's disease (AD) is characterized by cognitive, behavioral and intellectual deficits. The term mild cognitive impairment (MCI) is used to describe individuals whose cognitive impairment departing from their expectations for the age that does not interfere with daily activities. To diagnose these disorders, a combination of time-consuming, expensive tests that has difficulties for the target population are evaluated, moreover, the evaluation may yield subjective results. In the presented study, a novel methodology is developed for the automatic detection of AD and MCI using EEG signals. Methods: This study analyzed the EEGs of 35 subjects (16 MCI, 8 AD, 11 healthy control) with the developed algorithm. The algorithm consists of 3 methods for analysis, discrete wavelet transform(DWT), power spectral density (PSD) and coherence. In the first approach, DWT is applied to the signals to obtain major EEG sub-bands, afterward, PSD of each sub-band is calculated using Burg's method. In the second approach, interhemispheric coherence values are calculated. The variance and amplitude summation of each sub-bands' PSD and the amplitude summation of the coherence values corresponding to the major sub-bands are determined as features. Bagged Trees is selected as a classifier among the other tested classification algorithms. Data set is used to train the classifier with 5-fold cross-validation. Results: As a result, accuracy, sensitivity, and specificity of 96.5%, 96.21%, 97.96% are achieved respectively. Conclusion: In this study, we have investigated whether EEG can provide efficient clues about the neuropathology of Alzheimer's Disease and mild cognitive impairment for early and accurate diagnosis. Accordingly, a decision support system that produces reproducible and objective results with high accuracy is developed.Item Neurodegenerative disease detection and severity prediction using deep learning approaches(2021) Erdas, Cagatay Berke; Sumer, Emre; Kibaroglu, Seda; 0000-0002-3964-268X; AAJ-2956-2021; AGA-5711-2022Neurodegenerative diseases (NDDs) such as amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Parkinson's disease (PD) can manifest themselves anatomically by degeneration in the brain as well as motor symptoms. The motor symptoms can affect walking dynamics in a disease-specific fashion; characteristically they disrupt gait. As the severity of the disease increases, walking ability deteriorates. We examined the effect of NDDs such as ALS, HD, and PD on gait and developed a convolutional long short-term memory (ConvLSTM) and threedimensional convolutional learning network (3D CNN)-based approach to detecting neurodegenerative conditions and predicting disease severity.Item Neutrophil to lymphocyte ratio, stroke severity and short term clinical outcomes in acute ischemic stroke(2021) Iyigundogdu, Ilkin; Derle, Eda; Kibaroglu, Seda; Can, Ufuk; 0000-0001-7860-040X; 0000-0002-3964-268X; AAJ-2053-2021; AAJ-2956-2021Background: Neutrophil to lymphocyte ratio is an easily evaluated systemic inflammation indicator. However, there are limited reports on neutrophil to lymphocyte ratio and functional outcome in ischemic stroke. In this study, we aimed to evaluate the association of neutrophil to lymphocyte ratio and stroke severity, short term functional outcomes and mortality in patients with acute ischemic stroke. Methods: The clinical data of patients who were > 18 age-old and hospitalized with acute ischemic stroke in Baskent University Hospital, Ankara, Turkey between January 2018 and May 2019 were studied retrospectively. Neutrophil to lymphocyte ratio were measured. The neutrophil to lymphocyte ratio and National Institute of Health Stroke Scale (NIHSS) score at admission, mortality during hospitalization and Modified Rankin Scale (mRS) score at discharge of the patients with acute ischemic stroke were correlated. Results: Among the acute ischemic stroke patients due to the exclusion criteria, the data of 134 patients were evaluated. Median age of the patients were 76 +/- 12.5 years and 82 patients (61.2%) were male. The median NIHSS scores of the patients at admission was 5 +/- 4.5. Mortality during the hospitalization was seen in 8 patients (6%). The median neutrophil to lymphocyte ratio value of the patients at admission were found to be 2.6 +/- 3.4. Neutrophil to lymphocyte ratio and NIHSS scores of the patients at admission, duration of the hospitalization, mRS scores at discharge and mortality during hospitalization were found to be positively correlated. Conclusion: Neutrophil to lymphocyte ratio is a simple and easily measured marker and can be used as a potential indicator for prognosis in acute ischemic stroke. However further prospective multicenter investigations are required to confirm the role of neutrophil to lymphocyte ratio for predicting the prognosis in acute ischemic stroke patients.Item Intrathecal Methotrexate-Induced Posterior Reversible Encephalopathy Syndrome (PRES)(2014) Guler, Tulay; Cakmak, Ozden Yener; Toprak, Selami Kocak; Kibaroglu, Seda; Can, Ufuk; 24764745Item Clinical results of carotid artery stenting versus carotid endarterectomy(2016) Derle, Eda; Akinci, Tuba; Kibaroglu, Seda; Harman, Ali; Kural, Feride; Cinar, Pinar; Kilinc, Munire; Akay, Hakki T.; Can, Ufuk; Benli, Ulku S.; 0000-0002-7386-7110; 0000-0002-4226-4034; 0000-0002-9975-3170; 0000-0003-2122-1016; 0000-0002-3964-268X; 0000-0001-8689-417X; 27744460; AAI-8830-2021; AAJ-2956-2021; K-9824-2013; AAL-9808-2021; AAJ-4403-2021; AAJ-2999-2021Objective: To review our results of carotid artery stenting (CAS) and carotid endarterectomy (CEA). Methods: We evaluated the medical records of patients undergoing carotid artery revascularization procedure, between 2001 and 2013 in Baskent University Hospital, Ankara, Turkey. Carotid artery stenting or CEA procedures were performed in patients with asymptomatic carotid stenosis (=70%) or symptomatic stenosis (=50%). Demographic data, procedural details, and clinical outcomes were recorded. Primary outcome measures were in 30-day stroke/transient ischemic attacks (TIA)/amaurosis fugax or death. Secondary outcome measures were nerve injury, bleeding complications, length of stay in hospital, stroke, restenosis (ICA patency), and all-cause death during long-term follow-up. Results: One hundred ninety-four CEA and 115 CAS procedures were performed for symptomatic and/or asymptomatic carotid artery stenosis. There is no significant differences 30-day mortality and neurologic morbidity between CAS (13%) and CEA procedures (7.7%). Length of stay in hospital were significantly longer in CEA group (p=0.001). In the post-procedural follow up, only in symptomatic patients, restenosis rate was higher in the CEA group (p=.045). The other endpoints did not differ significantly. Conclusions: Endovascular stent treatment of carotid artery atherosclerotic disease is an alternative for vascular surgery, especially for patients that are high risk for standard CEA. The increasing experience, development of cerebral protection systems and new treatment protocols increases CAS feasibility.Item The Assessment of Efficiency of Traditional and Complementary Medicine Practices in Neurology(2018) Kibaroglu, Seda; Caglayan, Hale Zeynep Batur; Ataoglu, Esra ErkocTraditional, complementary, and alternative medicine practices are used in the prevention, diagnosis, and treatment of a wide variety of diseases in the world. Such practices in Turkey are regulated by the "Regulation of Traditional and Complementary Medicine Practice" issued by the Ministry of Health in the Official Gazette of the Republic of Turkey (Issue: 29158, 27th October 2014). The appendix of this regulation defines 15 practices that can be applied in units and practice centers. These applications include; 1. Acupuncture, 2. Apitherapy, 3. Phytotherapy, 4. Hypnosis, 5. Leech therapy (Hirudotherapy), 6. Homeopathy, 7. Chiropractic, 8. Cupping, 9. Maggot therapy, 10. Mesotherapy, 11. Prolotherapy, 12. Osteopathy, 13. Ozone therapy, 14. Reflexology, and 15. Music therapy. In this review, the indications of these 15 applications in the field of neurology are examined and current opinions of the evidence-based medical data are summarized.