AI Medical Compendium Topic

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Stroke

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Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models.

Current neurology and neuroscience reports
PURPOSE OF REVIEW: Artificial intelligence (AI) promises to compress stroke treatment timelines, yet its clinical return on investment remains uncertain. We interrogate state‑of‑the‑art AI platforms across imaging, workflow orchestration, and outcome...

Machine learning-based prediction of 90-day prognosis and in-hospital mortality in hemorrhagic stroke patients.

Scientific reports
This study aims to predict hemorrhagic stroke outcomes, including 90-day prognosis and in-hospital mortality, using machine learning models and SHapley Additive exPlanations (SHAP) analysis. Data were collected from a national Stroke Registry from Ja...

Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods.

Scientific reports
An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation, to their families, and healthcare providers. This study aimed at developing and validating externally patient-wise prognostic models of the global fu...

Machine Learning Assisted Stroke Prediction in Mechanical Circulatory Support: Predictive Role of Systemic Mitochondrial Dysfunction.

ASAIO journal (American Society for Artificial Internal Organs : 1992)
Stroke continues to be a major adverse event in advanced congestive heart failure (CHF) patients after continuous-flow left ventricular assist device (CF-LVAD) implantation. Abnormalities in mitochondrial oxidative phosphorylation (OxPhos) have been ...

Predicting the risks of stroke, cardiovascular disease, and peripheral vascular disease among people with type 2 diabetes with artificial intelligence models: A systematic review and meta-analysis.

Narra J
Macrovascular complications, including stroke, cardiovascular disease (CVD), and peripheral vascular disease (PVD), significantly contribute to morbidity and mortality in individuals with type 2 diabetes mellitus (T2DM). The aim of this study was to ...

Speckle pattern analysis with deep learning for low-cost stroke detection: a phantom-based feasibility study.

Journal of biomedical optics
SIGNIFICANCE: Stroke is a leading cause of disability worldwide, necessitating rapid and accurate diagnosis to limit irreversible brain damage. However, many advanced imaging modalities (computerized tomography, magnetic resonance imaging) remain ina...

Identifying individuals at risk of post-stroke depression: Development and validation of a predictive model.

Saudi medical journal
OBJECTIVES: To identify the factors associated with post-stroke depression (PSD) and develop a machine learning predictive model using a large dataset, considering sociodemographic, lifestyle, and clinical factors.

Effects of exoskeleton rehabilitation robot training on neuroplasticity and lower limb motor function in patients with stroke.

BMC neurology
BACKGROUND: Lower limb exoskeleton rehabilitation robot is a new technology to improve the lower limb motor function of stroke patients. Recovery of motor function after stroke is closely related to neuroplasticity in the motor cortex and associated ...

Multitask learning multimodal network for chronic disease prediction.

Scientific reports
Chronic diseases are a critical focus in the management of elderly health. Early disease prediction plays a vital role in achieving disease prevention and reducing the associated burden on individuals and healthcare systems. Traditionally, separate m...

Electromechanical-assisted training for walking after stroke.

The Cochrane database of systematic reviews
RATIONALE: Walking difficulties are common after a stroke. During rehabilitation, electromechanical and robotic gait-training devices can help improve walking. As the evidence and certainty of the evidence may have changed since our last update in 20...