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A Platform for Collection and Analysis of Image Data on Stroke.
Authors: Dolotova D, Donitova V, Arhipov I, Sharifullin F, Zagriazkina T, Kobrinskii B, Gavrilov A Abstract Identifying imaging biomarkers (IBs) of stroke remains a priority in neurodiagnostics. There is a number of different methods for image analysis and learning rules applicable in this field, but all of them require large arrays of DICOM images and clinical data. In order to amass such dataset,we havedesigneda platform for systematic collection of clinical data and medical images in different modalities. The platform provides easy-to-use tools to create formalized radiology reports, contour and tag the regions ...
Source: Studies in Health Technology and Informatics - July 28, 2019 Category: Information Technology Tags: Stud Health Technol Inform Source Type: research

Computer Imagery and Neurological Rehabilitation: On the Use of Augmented Reality in Sensorimotor Training to Step Up Naturally Occurring Cortical Reorganization in Patients Following Stroke.
Authors: Correa-Agudelo E, Ferrin C, Velez P, Gomez JD Abstract This work promotes the use of computer-generated imagery -as visual illusions- to speed up motor learning in rehabilitation. In support of this, we adhere the principles of experience-dependent neuroplasticity and the positive impact of virtual reality (VR) thereof. Specifically, post-stroke patients will undergo motor therapy with a surrogate virtual limb that fakes the paralyzed limb. Along these lines, their motor intentions will match the visual evidence, which fosters physiological, functional and structural changes over time, for recovery of lost...
Source: Studies in Health Technology and Informatics - April 6, 2016 Category: Information Technology Tags: Stud Health Technol Inform Source Type: research

Multivariable Risk Prediction of Dysphagia in Hospitalized Patients Using Machine Learning.
CONCLUSION: The developed models outperformed previously published models predicting dysphagia. In future, an implementation in the clinical workflow is needed to determine the clinical benefit. PMID: 32578538 [PubMed - in process]
Source: Studies in Health Technology and Informatics - June 26, 2020 Category: Information Technology Tags: Stud Health Technol Inform Source Type: research

Machine Learning Based Risk Prediction for Major Adverse Cardiovascular Events
CONCLUSION: The developed risk prediction models achieved an excellent performance in the test data. Future research is needed to determine the performance of these models and their clinical benefit in prospective settings.PMID:33965930 | DOI:10.3233/SHTI210100
Source: Studies in Health Technology and Informatics - May 9, 2021 Category: Information Technology Authors: Michael Schrempf Diether Kramer Stefanie Jauk Sai P K Veeranki Werner Leodolter Peter P Rainer Source Type: research

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