3 research outputs found

    Use of Machine Learning Classifiers based on Visual Metrics in Children with Acquired Demyelinating Syndromes

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    Multiple Sclerosis (MS), an inflammatory degenerative disease and the visual pathway is a key target in the search for a reliable and easily obtainable diagnostic biomarker that can aid the diagnosis. The objective of this study was to investigate the utility of machine learning (ML) based on optical coherence tomography (OCT) features to identify children with MS and other ADS. In this cross-sectional study a total of 512 eyes from 69 (neyes = 138) healthy controls and 187 (neyes = 374) children with ADS were included. Random forest classifier with recursive feature elimination identified MS with 80% accuracy. A set of eight retinal features were identified as the most important in this classification. In conclusion, this study demonstrated that ML classifiers can be used to diagnose MS in children based on structural OCT measures alone with high accuracy, sensitivity and specificity.M.Sc

    Investigation of the effects of continuous theta burst transcranial magnetic stimulation in patients with migraine

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    Objective: Repetitive transcranial magnetic stimulation (rTMS) allows the non-invasive investigation of synaptic plasticity. Theta-burst stimulation (TBS) is a modified form of rTMS that induces synaptic plasticity. Our objective was to evaluate cortical excitability using paired-pulse transcranial magnetic stimulation (ppTMS) before and after continuous TBS (cTBS) in healthy controls and patients with migraine
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