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Permanent URI for this collectionhttps://hdl.handle.net/11443/932

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Now showing 1 - 4 of 4
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    AO Spine Adult Spinal Deformity Patient Profile: A Paradigm Shift in Comprehensive Patient Evaluation in Order to Optimize Treatment and Improve Patient Care
    (SAGE PUBLICATIONS LTD, 2022-01-01) Naresh-Babu, J.; Kwan, Kenny Yat Hong; Wu, Yabin; Yilgor, Caglar; Alanay, Ahmet; Cheung, Kenneth M. C.; Polly Jr., David W.; Park, Jong-Beom; Ito, Manabu; Lenke, Lawrence G.; van Hooff, Miranda L.; de Kleuver, Marinus; Deformity, A.O. Spine Knowledge Forum
    Study Design: Modified Delphi study. Objective: Adult spinal deformity (ASD) is an increasingly recognized condition, comprising a spectrum of pathologies considerably impacting patients' health and functional status. Patients present with a combination of pain, disability, comorbidities and radiological deformity. The study aims to propose a systematic approach of gathering information on the factors that drive decision-making by developing a patient profile. Methods: The present study comprises of 3 parts. Part 1: Development of prototype of patient profile: The data from the Core Outcome Study on SCOlisis (COSSCO) by Scoliosis Research Society (SRS) was categorized into a conceptual framework. Part 2: Modified Delphi study: Items reaching >70\% agreement were included in a 4 round iterative process with 51 panellists across the globe. Part 3: Pilot testing-feasibility: Content validity and usability were evaluated quantitatively. Results: The profile consisted of 4 domains. 1. General health with demographics and comorbidities, 2.Spine-specific health with spine related health and neurological status, 3. Imaging with radiographic and MRI parameters and 4. Deformity type. Each domain consisted of 1 or 2 components with various factors and their measuring instruments. Profile was found to have an excellent content validity (1-CVIr 0.78-1.00
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    Invertebrate Iridoviruses: A Glance over the Last Decade
    (MDPI, 2018-01-01) Ince, Ikbal Agah; Ozcan, Orhan; Ilter-Akulke, Ayca Zeynep; Scully, Erin D.; Ozgen, Arzu
    Members of the family Iridoviridae (iridovirids) are large dsDNA viruses that infect both invertebrate and vertebrate ectotherms and whose symptoms range in severity from minor reductions in host fitness to systemic disease and large-scale mortality. Several characteristics have been useful for classifying iridoviruses
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    Three-Dimensional Micro Structure of the Cingulum Bundle: A Fiber Dissection Study
    (YERKURE TANITIM \& YAYINCILIK HIZMETLERI A S, 2019-01-01) Gungor, Abuzer; Hasimoglu, Ozan; Cirak, Musa; Erkan, Buruc
    Objective: Cingulum bundle is a major fiber tract that extends into the cingulate gyrus and reaches to the temporal tip. It carries striatal, projection, association and commissural fibers. Therefore, it has an important role in the diagnosis and treatment of many psychiatric and neurological diseases. Our aim
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    Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers
    (MDPI, 2022-01-01) Erdogan, Sinem Burcu; Yukselen, Gulnaz
    Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naive bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81\% and specificities above 94\%. SVM had the highest performance in terms of accuracy (85.1 +/- 1.77\%), sensitivity (84 +/- 1.7\%), specificity (95 +/- 0.5\%), precision (86 +/- 1.6\%), and F1-score (85 +/- 1.7\%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.