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Stroke

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Towards Rapid and Low-Cost Stroke Detection Using SERS and Machine Learning.

Biosensors
Stroke affects approximately 12 million individuals annually, necessitating swift diagnosis to avert fatal outcomes. Current hospital imaging protocols often delay treatment, underscoring the need for portable diagnostic solutions. We have investigat...

The effect of lower limb rehabilitation robot on lower limb -motor function in stroke patients: a systematic review and meta-analysis.

Systematic reviews
BACKGROUND: The assessment and enhancement of lower limb motor function in hemiplegic patients is of paramount importance. The emergence of lower limb rehabilitation robots offers a promising avenue for improving motor function in these patients, add...

Predictive power of artificial intelligence for malignant cerebral edema in stroke patients: a CT-based systematic review and meta-analysis of prevalence and diagnostic performance.

Neurosurgical review
Malignant cerebral edema (MCE) is a severe complication of acute ischemic stroke, with high mortality rates. Early and accurate prediction of MCE is critical for initiating timely interventions such as decompressive hemicraniectomy. Artificial intell...

Achieving precision assessment of functional clinical scores for upper extremity using IMU-Based wearable devices and deep learning methods.

Journal of neuroengineering and rehabilitation
Stroke is a serious cerebrovascular disease, and rehabilitation following the acute phase is particularly crucial. Not all rehabilitation outcomes are favorable, highlighting the necessity for personalized rehabilitation. Precision assessment is esse...

Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images.

Scientific reports
Stroke is a main risk to life and fitness in current society, particularly in the aging population. Also, the stroke is recognized as a cerebrovascular accident. It contains a nervous illness, which can result from haemorrhage or ischemia of the brai...

A deep learning approach for quantifying CT perfusion parameters in stroke.

Biomedical physics & engineering express
. Computed tomography perfusion (CTP) imaging is widely used for assessing acute ischemic stroke. However, conventional methods for quantifying CTP images, such as singular value decomposition (SVD), often lead to oscillations in the estimated residu...

Interim results of exoskeletal wearable robot for gait recovery in subacute stroke patients.

Scientific reports
Exoskeletons have been proposed for potential clinical use to improve ambulatory function in patients with stroke. The aim of an interim analysis of an international, multicenter, randomized, controlled trial was to investigate the short-term effect ...

An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment.

Scientific reports
Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, and continuous monitoring and early detection are necessary for effective management. The Metaverse Environment (ME) has gained attention in the digital healthcare e...

An innovative model based on machine learning and fuzzy logic for tracking lower limb exercises in stroke patients.

Scientific reports
Rehabilitation after a stroke is vital for regaining functional abilities. However, a shortage of rehabilitation professionals leads to many patients with severe disabilities. Traditional rehabilitation methods can be time-consuming and hard to measu...

Evaluating Performance of a Deep Learning Multilabel Segmentation Model to Quantify Acute and Chronic Brain Lesions at MRI after Stroke and Predict Prognosis.

Radiology. Artificial intelligence
Purpose To develop and evaluate a multilabel deep learning network to identify and quantify acute and chronic brain lesions at multisequence MRI after acute ischemic stroke (AIS) and assess relationships between clinical and model-extracted radiologi...