AIMC Topic: Stroke
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Machine Learning Approach to Identify Stroke Within 4.5 Hours.
Stroke
Jan 28, 2020
Background and Purpose- We aimed to investigate the ability of machine learning (ML) techniques analyzing diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging to identify patients within the reco...
The Role of Robotic Path Assistance and Weight Support in Facilitating 3D Movements in Individuals With Poststroke Hemiparesis.
Neurorehabilitation and neural repair
Jan 20, 2020
. High-intensity repetitive training is challenging to provide poststroke. Robotic approaches can facilitate such training by unweighting the limb and/or by improving trajectory control, but the extent to which these types of assistance are necessary...
Clot Analog Attenuation in Non-contrast CT Predicts Histology: an Experimental Study Using Machine Learning.
Translational stroke research
Jan 14, 2020
Exact histological clot composition remains unknown. The purpose of this study was to identify the best imaging variables to be extrapolated on clot composition and clarify variability in the imaging of thrombi by non-contrast CT. Using a CT-phantom ...
Assessing stroke severity using electronic health record data: a machine learning approach.
BMC medical informatics and decision making
Jan 8, 2020
BACKGROUND: Stroke severity is an important predictor of patient outcomes and is commonly measured with the National Institutes of Health Stroke Scale (NIHSS) scores. Because these scores are often recorded as free text in physician reports, structur...
Reliability, validity and discriminant ability of a robotic device for finger training in patients with subacute stroke.
Journal of neuroengineering and rehabilitation
Jan 3, 2020
BACKGROUND: The majority of stroke survivors experiences significant hand impairments, as weakness and spasticity, with a severe impact on the activity of daily living. To objectively evaluate hand deficits, quantitative measures are needed. The aim ...
AI-based computer-aided diagnosis (AI-CAD): the latest review to read first.
Radiological physics and technology
Jan 2, 2020
The third artificial intelligence (AI) boom is coming, and there is an inkling that the speed of its evolution is quickly increasing. In games like chess, shogi, and go, AI has already defeated human champions, and the fact that it is able to achieve...
Real-Time Detection of Compensatory Patterns in Patients With Stroke to Reduce Compensation During Robotic Rehabilitation Therapy.
IEEE journal of biomedical and health informatics
Jan 1, 2020
OBJECTIVES: Compensations are commonly employed by patients with stroke during rehabilitation without therapist supervision, leading to suboptimal recovery outcomes. This study investigated the feasibility of the real-time monitoring of compensation ...
Development of a Novel Prognostic Model to Predict 6-Month Swallowing Recovery After Ischemic Stroke.
Stroke
Dec 30, 2019
Background and Purpose- The aim of this study was to explore clinical and radiological prognostic factors for long-term swallowing recovery in patients with poststroke dysphagia and to develop and validate a prognostic model using a machine learning ...
White Matter
Enteral Nutrition
Male
Basal Ganglia
Prognosis
Internal Capsule
Fluoroscopy
Proportional Hazards Models
Brain Stem
Middle Aged
Humans
Female
Stroke
Magnetic Resonance Imaging
Multivariate Analysis
Machine Learning
Reproducibility of Results
Sex Factors
Cerebral Cortex
Algorithms
Recovery of Function
Aged
Brain Ischemia
Deglutition Disorders
Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke.
Stroke
Dec 30, 2019
Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magne...
Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks.
Journal of neurointerventional surgery
Dec 23, 2019
BACKGROUND AND PURPOSE: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in cli...
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