Scopus Açık Erişimli Yayınlar

Permanent URI for this collectionhttps://hdl.handle.net/11727/10760

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Author: search.filters.author.Kibaroglu, Sedasearch.filters.applied.f.publicationcategory: search.filters.publicationcategory.Makale - Uluslararası Hakemli Dergi

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    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-5711-2022
    Neurodegenerative 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.
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    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-2956-2021
    Background: 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.