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Cerebral Infarction

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Prediction Model between Serum Vitamin D and Neurological Deficit in Cerebral Infarction Patients Based on Machine Learning.

Computational and mathematical methods in medicine 35799673
OBJECTIVE: Vitamin D is associated with neurological deficits in patients with cerebral infarction. This study uses machine learning to evaluate the prediction model's efficacy of the correlation between vitamin D and neurological deficit in patients...
Area Under Curve Cerebral Infarction Humans Logistic Models Machine Learning Retrospective Studies Vitamin D
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Development of a machine learning-based risk prediction model for cerebral infarction and comparison with nomogram model.

Journal of affective disorders 35882300
BACKGROUND: Development of a cerebral infarction (CI) risk prediction model by mining routine test big data with machine learning algorithms.
Cerebral Infarction Humans Logistic Models Machine Learning Middle Aged Nomograms Support Vector Machine
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An Explainable Artificial Intelligence Model to Predict Malignant Cerebral Edema after Acute Anterior Circulating Large-Hemisphere Infarction.

European neurology 38565087
INTRODUCTION: Malignant cerebral edema (MCE) is a serious complication and the main cause of poor prognosis in patients with large-hemisphere infarction (LHI). Therefore, the rapid and accurate identification of potential patients with MCE is essenti...
Aged Aged, 80 and over Artificial Intelligence Brain Edema Cerebral Infarction Female Humans Machine Learning Male Middle Aged Prognosis Retrospective Studies
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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 37980594
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...
Alberta Brain Ischemia Cerebral Infarction Deep Learning Humans Ischemic Stroke Radiomics Retrospective Studies Stroke Tomography, X-Ray Computed
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Application of interpretable machine learning algorithms to predict acute kidney injury in patients with cerebral infarction in ICU.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association 38657830
BACKGROUND: Acute kidney injury (AKI) is not only a complication but also a serious threat to patients with cerebral infarction (CI). This study aimed to explore the application of interpretable machine learning algorithms in predicting AKI in patien...
Acute Kidney Injury Aged Aged, 80 and over Cerebral Infarction China Decision Support Techniques Diagnosis, Computer-Assisted Female Humans Intensive Care Units Machine Learning Male Middle Aged Predictive Value of Tests Prognosis Reproducibility of Results Retrospective Studies Risk Assessment Risk Factors
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Development and validation of machine learning models to predict postoperative infarction in moyamoya disease.

Journal of neurosurgery 38579355
OBJECTIVE: Cerebral infarction is a common complication in patients undergoing revascularization surgery for moyamoya disease (MMD). Although previous statistical evaluations have identified several risk factors for postoperative brain ischemia, the ...
Adult Cerebral Infarction Cerebral Revascularization Female Humans Machine Learning Male Middle Aged Moyamoya Disease Postoperative Complications Predictive Value of Tests Retrospective Studies Support Vector Machine Young Adult
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Development of machine learning-based models for predicting risk factors in acute cerebral infarction patients: a clinical retrospective study.

BMC neurology 39217304
OBJECTIVES: The aim of this study was to develop machine learning-based models for predicting acute cerebral infarction (ACI) in patients.
Aged Cerebral Infarction Female Humans Logistic Models Machine Learning Male Middle Aged Retrospective Studies Risk Factors
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Comparative Assessment of Manual Segmentation of Cerebral Infarction Lesions in Experimental Animals Based on Magnetic Resonance Imaging Using Artificial Intelligence.

Bulletin of experimental biology and medicine 40148667
The aim of this study was to evaluate the quality of manual segmentation of cerebral infarction lesions in experimental animals with modeled brain infarct based on magnetic resonance imaging compared to an automated artificial intelligence approach. ...
Animals Artificial Intelligence Brain Cerebral Infarction Disease Models, Animal Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Rats Reproducibility of Results
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Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests.

Scientific reports 40240412
Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability and death worldwide. Transient ischemic attack (TIA) is a strong predictor of subsequent stroke. Individuals with dysmetabolism, such as hypertension, h...
Adult Aged Biomarkers Cerebral Infarction Female Hematologic Tests Humans Ischemic Attack, Transient Machine Learning Male Middle Aged Risk Factors ROC Curve
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Deep learning-based automatic segmentation of cerebral infarcts on diffusion MRI.

Scientific reports 40240396
We explored effects of (1) training with various sample sizes of multi-site vs. single-site training data, (2) cross-site domain adaptation, and (3) data sources and features on the performance of algorithms segmenting cerebral infarcts on Magnetic R...
Algorithms Cerebral Infarction Deep Learning Diffusion Magnetic Resonance Imaging Humans Image Processing, Computer-Assisted
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