AI Medical Compendium Topic:
Stroke

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Extreme Gradient Boosting Model Has a Better Performance in Predicting the Risk of 90-Day Readmissions in Patients with Ischaemic Stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECT: Ischemic stroke readmission within 90 days of hospital discharge is an important quality of care metric. The readmission rates of ischemic stroke patients are usually higher than those of patients with other chronic diseases. Our aim was to i...

Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks.

Computers in biology and medicine
The use of Computed Tomography (CT) imaging for patients with stroke symptoms is an essential step for triaging and diagnosis in many hospitals. However, the subtle expression of ischemia in acute CT images has made it hard for automated methods to e...

Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review.

Journal of neurointerventional surgery
BACKGROUND AND PURPOSE: Acute stroke caused by large vessel occlusions (LVOs) requires emergent detection and treatment by endovascular thrombectomy. However, radiologic LVO detection and treatment is subject to variable delays and human expertise, r...

Physiological responses and perceived exertion during robot-assisted treadmill walking in non-ambulatory stroke survivors.

Disability and rehabilitation
PURPOSE: To examine physiological responses and perceived exertion during robot-assisted treadmill walking in non-ambulatory stroke survivors; compare these outcomes with aerobic exercise recommendations; and investigate the effect of robotic assista...

A network underlying human higher-order motor control: Insights from machine learning-based lesion-behaviour mapping in apraxia of pantomime.

Cortex; a journal devoted to the study of the nervous system and behavior
Neurological patients with apraxia of pantomime provide us with a unique opportunity to study the neural correlates of higher-order motor function. Previous studies using lesion-behaviour mapping methods led to inconsistent anatomical results, report...

Applying density-based outlier identifications using multiple datasets for validation of stroke clinical outcomes.

International journal of medical informatics
INTRODUCTION: Clinicians commonly use the modified Rankin Scale (mRS) and the Barthel Index (BI) to measure clinical outcome after stroke. These are potential targets in machine learning models for stroke outcome prediction. Therefore, the quality of...

Predicting post-stroke pneumonia using deep neural network approaches.

International journal of medical informatics
BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely and accurate prediction of post-stroke pneumonia would be highly valuable in clinical practice. Pre...

Machine Learning-Enabled Automated Determination of Acute Ischemic Core From Computed Tomography Angiography.

Stroke
Background and Purpose- The availability of and expertise to interpret advanced neuroimaging recommended in the guideline-based endovascular stroke therapy (EST) evaluation are limited. Here, we develop and validate an automated machine learning-base...

Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway.

BMJ open
OBJECTIVE: To evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals.