AIMC Topic: Stroke

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New Artificial Intelligence-Integrated Electromyography-Driven Robot Hand for Upper Extremity Rehabilitation of Patients With Stroke: A Randomized, Controlled Trial.

Neurorehabilitation and neural repair
BACKGROUND: An artificial intelligence (AI)-integrated electromyography (EMG)-driven robot hand was devised for upper extremity (UE) rehabilitation. This robot detects patients' intentions to perform finger extension and flexion based on the EMG acti...

Deep learning prediction of motor performance in stroke individuals using neuroimaging data.

Journal of biomedical informatics
The degree of motor impairment and profile of recovery after stroke are difficult to predict for each individual. Measures obtained from clinical assessments, as well as neurophysiological and neuroimaging techniques have been used as potential bioma...

A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform.

Sensors (Basel, Switzerland)
Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care...

NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform.

Sensors (Basel, Switzerland)
BACKGROUND: This article presents the system architecture and validation of the NeuroSuitUp body-machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neuro...

Ultrafast MRI using deep learning echoplanar imaging for a comprehensive assessment of acute ischemic stroke.

European radiology
OBJECTIVES: Acute ischemic stroke (AIS) is an emergency requiring both fast and informative MR sequences. We aimed to assess the performance of an artificial intelligence-enhanced ultrafast (UF) protocol, compared to the reference protocol, in the AI...

Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this...

Effects of Upper Limb Robot-Assisted Rehabilitation Compared with Conventional Therapy in Patients with Stroke: Preliminary Results on a Daily Task Assessed Using Motion Analysis.

Sensors (Basel, Switzerland)
Robotic rehabilitation of the upper limb has demonstrated promising results in terms of the improvement of arm function in post-stroke patients. The current literature suggests that robot-assisted therapy (RAT) is comparable to traditional approaches...

Is the robotic rehabilitation that is added to intensive body rehabilitation effective for maximization of upper extremity motor recovery following a stroke? A randomized controlled study.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND: Trunk stabilization, which is a factor that directly affects the performance of affected upper-limb movements in stroke patients, is of critical importance in the performance of selective motor control.

Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
The last decade has seen significant advances in the accumulation of medical data, the computational techniques to analyze that data, and corresponding improvements in management. Interventions such as thrombolytics and mechanical thrombectomy improv...