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Ischemic Stroke

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RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields.

NeuroImage
Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy. However, due to noise, motion, and partial volume effects, automated segmentation of lesi...

Evaluating Performance of a Deep Learning Multilabel Segmentation Model to Quantify Acute and Chronic Brain Lesions at MRI after Stroke and Predict Prognosis.

Radiology. Artificial intelligence
Purpose To develop and evaluate a multilabel deep learning network to identify and quantify acute and chronic brain lesions at multisequence MRI after acute ischemic stroke (AIS) and assess relationships between clinical and model-extracted radiologi...

Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.

Radiology
Background Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, requiring swift and precise clinical decisions based on neuroimaging. Recent advances in deep learning-based computer vision and language artificial intelligence (AI)...

Embolic Ischemic Cortical Stroke in a Young Flight Instructor with a Small Patent Foramen Ovale.

Aerospace medicine and human performance
BACKGROUND: Stroke in young patients is frequently associated with a patent foramen ovale (PFO). Controversy exists over whether the PFO is a cause, a risk factor, or an incidental finding. Estimating the individualized risk of stroke recurrence has ...

Predicting functional outcome in ischemic stroke patients using genetic, environmental, and clinical factors: a machine learning analysis of population-based prospective cohort study.

Briefings in bioinformatics
Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality of life for patients. Accurately predicting the IS functional outcome is crucial for precise risk stratification and effective therapeutic interventi...

Multimodal ischemic stroke recurrence prediction model based on the capsule neural network and support vector machine.

Medicine
Ischemic stroke (IS) has a high recurrence rate. Machine learning (ML) models have been developed based on single-modal biochemical tests, and imaging data have been used to predict stroke recurrence. However, the prediction accuracy of these models ...

Segmentation of stroke lesions using transformers-augmented MRI analysis.

Human brain mapping
Accurate segmentation of chronic stroke lesions from mono-spectral magnetic resonance imaging scans (e.g., T1-weighted images) is a difficult task due to the arbitrary shape, complex texture, variable size and intensities, and varied locations of the...

Ultrafast Brain MRI with Deep Learning Reconstruction for Suspected Acute Ischemic Stroke.

Radiology
Background Deep learning (DL)-accelerated MRI can substantially reduce examination times. However, studies prospectively evaluating the diagnostic performance of DL-accelerated MRI reconstructions in acute suspected stroke are lacking. Purpose To inv...

Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accurate prediction to optimize patient care. A comprehensive search was conducted across PubMed, Web of...

A deep learning and radiomics based Alberta stroke program early CT score method on CTA to evaluate acute ischemic stroke.

Journal of X-ray science and technology
BACKGROUND: Alberta stroke program early CT score (ASPECTS) is a semi-quantitative evaluation method used to evaluate early ischemic changes in patients with acute ischemic stroke, which can guide physicians in treatment decisions and prognostic judg...