AIMC Topic: Stroke
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A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study.
Scientific reports
Oct 15, 2021
High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value...
A deep learning-based model for prediction of hemorrhagic transformation after stroke.
Brain pathology (Zurich, Switzerland)
Oct 4, 2021
Hemorrhagic transformation (HT) is one of the most serious complications after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) patients. The purpose of this study is to develop and validate deep-learning (DL) models based on multiparam...
A Tenodesis-Induced-Grip exoskeleton robot (TIGER) for assisting upper extremity functions in stroke patients: a randomized control study.
Disability and rehabilitation
Sep 29, 2021
PURPOSE: This study was aimed toward developing a lightweight assisting tenodesis-induced-grip exoskeleton robot (TIGER) and to examine the performance of the TIGER in stroke patients with hemiplegia.
Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation.
Scientific reports
Sep 23, 2021
Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed t...
Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement.
Scientific reports
Sep 21, 2021
Cerebrovascular events (CVE) are among the most feared complications of transcatheter aortic valve replacement (TAVR). CVE appear difficult to predict due to their multifactorial origin incompletely explained by clinical predictors. We aimed to build...
Home-based self-help telerehabilitation of the upper limb assisted by an electromyography-driven wrist/hand exoneuromusculoskeleton after stroke.
Journal of neuroengineering and rehabilitation
Sep 15, 2021
BACKGROUND: Most stroke survivors have sustained upper limb impairment in their distal joints. An electromyography (EMG)-driven wrist/hand exoneuromusculoskeleton (WH-ENMS) was developed previously. The present study investigated the feasibility of a...
Image Features of Magnetic Resonance Angiography under Deep Learning in Exploring the Effect of Comprehensive Rehabilitation Nursing on the Neurological Function Recovery of Patients with Acute Stroke.
Contrast media & molecular imaging
Sep 10, 2021
This study was to explore the effects of imaging characteristics of magnetic resonance angiography (MRA) based on deep learning on the comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke. In this study, 84 ...
BEAGLE-A Kinematic Sensory System for Objective Hand Function Assessment in Technology-Mediated Rehabilitation.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 10, 2021
We present a hand functions assessment system (BEAGLE) for kinematic tracking of hand and finger movements, envisioned as a technology-mediated rehabilitation tool. The system is custom-designed for fast and easy placement on an impaired hand (spasti...
Walking with robot-generated haptic forces in a virtual environment: a new approach to analyze lower limb coordination.
Journal of neuroengineering and rehabilitation
Sep 9, 2021
BACKGROUND: Walking with a haptic tensile force applied to the hand in a virtual environment (VE) can induce adaptation effects in both chronic stroke and non-stroke individuals. These effects are reflected in spatiotemporal outcomes such as gait spe...
MTANS: Multi-Scale Mean Teacher Combined Adversarial Network with Shape-Aware Embedding for Semi-Supervised Brain Lesion Segmentation.
NeuroImage
Sep 8, 2021
The annotation of brain lesion images is a key step in clinical diagnosis and treatment of a wide spectrum of brain diseases. In recent years, segmentation methods based on deep learning have gained unprecedented popularity, leveraging a large amount...
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