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Stroke

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Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks.

Scientific reports
Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used m...

Combining robot-assisted therapy with virtual reality or using it alone? A systematic review on health-related quality of life in neurological patients.

Health and quality of life outcomes
BACKGROUND: In the field of neurorehabilitation, robot-assisted therapy (RAT) and virtual reality (VR) have so far shown promising evidence on multiple motor and functional outcomes. The related effectiveness on patients' health-related quality of li...

The effect of robot-assisted gait training frequency on walking, functional recovery, and quality of life in patients with stroke.

Acta neurologica Belgica
AIM: This study aims to investigate the effects of robot-assisted gait training (RAGT) frequency on walking, functional recovery, QoL and mood.

The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke.

Journal of neuroengineering and rehabilitation
BACKGROUND: Robots can generate rich kinematic datasets that have the potential to provide far more insight into impairments than standard clinical ordinal scales. Determining how to define the presence or absence of impairment in individuals using k...

Effect of cyborg-type robot Hybrid Assistive Limb on patients with severe walking disability in acute stroke: A randomized controlled study.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: To investigate whether early gait training using Hybrid Assistive Limb (HAL) is feasible and improves walking and independency compared with conventional physical therapy (CPT) in patients with severe walking disability after stroke.

Deep learning-based personalised outcome prediction after acute ischaemic stroke.

Journal of neurology, neurosurgery, and psychiatry
BACKGROUND: Whether deep learning models using clinical data and brain imaging can predict the long-term risk of major adverse cerebro/cardiovascular events (MACE) after acute ischaemic stroke (AIS) at the individual level has not yet been studied.

Deep learning for collateral evaluation in ischemic stroke with imbalanced data.

International journal of computer assisted radiology and surgery
PURPOSE: Collateral evaluation is typically done using visual inspection of cerebral images and thus suffers from intra- and inter-rater variability. Large open databases of ischemic stroke patients are rare, limiting the use of deep learning methods...

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data.

Sensors (Basel, Switzerland)
Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluc...