AIMC Topic: Brain Ischemia

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A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging.

IEEE transactions on medical imaging
Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as...

A Single Session of Robot-Controlled Proprioceptive Training Modulates Functional Connectivity of Sensory Motor Networks and Improves Reaching Accuracy in Chronic Stroke.

Neurorehabilitation and neural repair
BACKGROUND: Passive robot-generated arm movements in conjunction with proprioceptive decision making and feedback modulate functional connectivity (FC) in sensory motor networks and improve sensorimotor adaptation in normal individuals. This proof-of...

Constipation and risk of death and cardiovascular events.

Atherosclerosis
BACKGROUND AND AIMS: Constipation is one of the most frequent symptoms encountered in daily clinical practice and is implicated in the development of atherosclerosis, potentially through altered gut microbiota. However, little is known about its asso...

Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Alberta Stroke Program Early CT Score (ASPECTS) was devised as a systematic method to assess the extent of early ischemic change on noncontrast CT (NCCT) in patients with acute ischemic stroke (AIS). Our aim was to automate AS...

Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a robust multidimensional deep-learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q-spa...

Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage.

Journal of neurointerventional surgery
BACKGROUND AND PURPOSE: Delayed cerebral ischemia (DCI) is a severe complication in patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have been previously identified. However, their predictive value is generally low. We ...

JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.

AJR. American journal of roentgenology
OBJECTIVE: When treatment decisions are being made for patients with acute ischemic stroke, timely and accurate outcome prediction plays an important role. The optimal rehabilitation strategy also relies on long-term outcome predictions. The decision...

Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning.

Stroke
BACKGROUND AND PURPOSE: Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to d...