Journal of neuroengineering and rehabilitation
Apr 9, 2024
BACKGROUND: Unsupervised robot-assisted rehabilitation is a promising approach to increase the dose of therapy after stroke, which may help promote sensorimotor recovery without requiring significant additional resources and manpower. However, the un...
AJNR. American journal of neuroradiology
Apr 8, 2024
BACKGROUND AND PURPOSE: Predicting long-term clinical outcome in acute ischemic stroke is beneficial for prognosis, clinical trial design, resource management, and patient expectations. This study used a deep learning-based predictive model (DLPD) to...
Medical & biological engineering & computing
Apr 5, 2024
Accurately predicting the prognosis of ischemic stroke patients after discharge is crucial for physicians to plan for long-term health care. Although previous studies have demonstrated that machine learning (ML) shows reasonably accurate stroke outco...
. The incidence of stroke rising, leading to an increased demand for rehabilitation services. Literature has consistently shown that early and intensive rehabilitation is beneficial for stroke patients. Robot-assisted devices have been extensively st...
BACKGROUND: Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors' engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individ...
Journal of clinical hypertension (Greenwich, Conn.)
Mar 24, 2024
Apparent treatment-resistant hypertension (aTRH), defined as blood pressure (BP) that remains uncontrolled despite unconfirmed concurrent treatment with three antihypertensives, is associated with an increased risk of developing cardiovascular and re...
OBJECTIVES: We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automati...
PURPOSE: Clinical risk scores are essential for predicting outcomes in stroke patients. The advancements in deep learning (DL) techniques provide opportunities to develop prediction applications using magnetic resonance (MR) images. We aimed to devel...
Stroke survivors with hemiparesis require extensive home-based rehabilitation. Deep learning-based classifiers can detect actions and provide feedback based on patient data; however, this is difficult owing to data sparsity and heterogeneity. In this...
Stroke is a global health concern causing significant mortality. Survivors face physical, cognitive, and emotional challenges, affecting their life satisfaction and social participation. Robot-assisted gait training with virtual reality, like Lokomat...