Archives of physical medicine and rehabilitation
Nov 15, 2014
OBJECTIVE: To compare response to upper-limb treatment using robotics plus motor learning (ML) versus functional electrical stimulation (FES) plus ML versus ML alone, according to a measure of complex functional everyday tasks for chronic, severely i...
European journal of physical and rehabilitation medicine
Oct 31, 2014
BACKGROUND: Spasticity has a role of primary importance in functional motor recovery of upper limb after a stroke. The widespread intervention is the botulinum toxin neurolysis, however robotic training could have a role as useful addition to this co...
The development of an innovative functional assessment procedure based on the combination of electroencephalography (EEG) and robot-assisted upper limb devices may provide new insights into the dynamics of cortical reorganization promoted by rehabili...
Cerebral cortex (New York, N.Y. : 1991)
May 16, 2014
Conventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associate...
Journal of neuroengineering and rehabilitation
Jun 7, 2025
BACKGROUND: Proprioceptive impairments of the upper limb are common after stroke. These impairments are not typically addressed during assessment or rehabilitation. Currently, most robotic paradigms for training of the upper limb have focused solely ...
BACKGROUND AND OBJECTIVE: Stroke ranks among the leading causes of disability and death worldwide. Timely detection can reduce its impact. Machine learning delivers powerful tools for image‑based diagnosis. This study introduces StrokeNeXt, a lightwe...
According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 to 219/100,000. Continuous care can improve the quality of life of stroke patients and reduce the ra...
A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical ...
OBJECTIVE: To develop and validate deep learning models leveraging CT imaging for the prediction and classification of brain stroke conditions, with the potential to enhance accuracy and support clinical decision-making.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Jun 1, 2025
BACKGROUND: Stroke is a major cause of mortality and permanent disability worldwide. Precise prediction of post-stroke mortality is essential for guiding treatment decisions and rehabilitation planning. The ability of Machine learning models to proce...