BACKGROUND: Hemorrhagic transformation (HT) is commonly detected in acute ischemic stroke (AIS) and often leads to poor outcomes. Currently, there is no ideal tool for early prediction of HT risk. Recently, machine learning has gained traction in str...
Zhongguo zhen jiu = Chinese acupuncture & moxibustion
May 12, 2025
OBJECTIVE: To construct a prediction model for the clinical effect of eye acupuncture combined with rehabilitation therapy on ischemic stroke based on interpretable machine learning.
BACKGROUND: COVID-19 is a disease that affects people globally. Beyond affecting the respiratory system, COVID-19 patients are at an elevated risk for both venous and arterial thrombosis. This heightened risk contributes to an increased probability o...
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...
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)...
Aerospace medicine and human performance
Oct 1, 2024
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 ...
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...
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 ...
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...
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...
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