AIMC Topic: Cerebrovascular Disorders

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Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis.

Journal of medical Internet research Jul 18, 2025
BACKGROUND: Machine learning (ML) models may offer greater clinical utility than conventional risk scores, such as the Thrombolysis in Myocardial Infarction (TIMI) and Global Registry of Acute Coronary Events (GRACE) risk scores. However, there is a ...
Risk Assessment Percutaneous Coronary Intervention Myocardial Infarction Cerebrovascular Disorders Humans Machine Learning Risk Factors
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Experience of Cardiovascular and Cerebrovascular Disease Surgery Patients: Sentiment Analysis Using the Korean Bidirectional Encoder Representations from Transformers (KoBERT) Model.

JMIR medical informatics May 30, 2025
BACKGROUND: Cardiovascular and cerebrovascular diseases significantly contribute to global mortality and disability. The shift to outpatient postoperative care, accelerated by the COVID-19 pandemic, emphasizes the need for effective management of pos...
Humans COVID-19 Male Cardiovascular Diseases Emotions Cerebrovascular Disorders Republic of Korea Female SARS-CoV-2
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Application and optimization of the U-Net++ model for cerebral artery segmentation based on computed tomographic angiography images.

European journal of radiology Apr 27, 2025
Accurate segmentation of cerebral arteries on computed tomography angiography (CTA) images is essential for the diagnosis and management of cerebrovascular diseases, including ischemic stroke. This study implemented a deep learning-based U-Net++ mode...
Cerebral Arteries Radiographic Image Interpretation, Computer-Assisted Cerebrovascular Disorders Computed Tomography Angiography Cerebral Angiography Humans Reproducibility of Results Deep Learning
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Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study.

Journal of medical Internet research Apr 9, 2025
BACKGROUND: Considering that most patients with low or no significant risk factors can safely undergo noncardiac surgery without additional cardiac evaluation, and given the excessive evaluations often performed in patients undergoing intermediate or...
Humans Machine Learning Retrospective Studies Female Risk Factors Aged Cerebrovascular Disorders Surgical Procedures, Operative Male Risk Assessment Postoperative Complications
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Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning.

BMC medical informatics and decision making Apr 1, 2025
BACKGROUND: Cognitive impairment is common after a stroke, but it can often go undetected. In this study, we investigated whether using gait and dual tasks could help detect cognitive impairment after stroke.
Gait Analysis Neuropsychological Tests Support Vector Machine Gait Cerebrovascular Disorders Female Aged Aged, 80 and over Sensitivity and Specificity Male Middle Aged Cognitive Dysfunction Humans Machine Learning
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Accelerated intracranial time-of-flight MR angiography with image-based deep learning image enhancement reduces scan times and improves image quality at 3-T and 1.5-T.

Neuroradiology Mar 17, 2025
PURPOSE: Three-dimensional time-of-flight magnetic resonance angiography (TOF-MRA) is effective for cerebrovascular disease assessment, but clinical application is limited by long scan times and low spatial resolution. Recent advances in deep learnin...
Signal-To-Noise Ratio Magnetic Resonance Angiography Cerebrovascular Disorders Image Interpretation, Computer-Assisted Image Enhancement Female Aged Adult Male Middle Aged Deep Learning Imaging, Three-Dimensional Humans Retrospective Studies
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Risk prediction for elderly cognitive impairment by radiomic and morphological quantification analysis based on a cerebral MRA imaging cohort.

European radiology Jan 9, 2025
OBJECTIVE: To establish morphological and radiomic models for early prediction of cognitive impairment associated with cerebrovascular disease (CI-CVD) in an elderly cohort based on cerebral magnetic resonance angiography (MRA).
Cognitive Dysfunction Cohort Studies Predictive Value of Tests Cerebrovascular Disorders Radiomics Male Risk Assessment Magnetic Resonance Angiography Humans Retrospective Studies Female Risk Factors Aged Aged, 80 and over
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Intelligent cholinergic white matter pathways algorithm based on U-net reflects cognitive impairment in patients with silent cerebrovascular disease.

Stroke and vascular neurology Dec 30, 2024
BACKGROUND AND OBJECTIVE: The injury of the cholinergic white matter pathway underlies cognition decline in patients with silent cerebrovascular disease (SCD) with white matter hyperintensities (WMH) of vascular origin. However, the evaluation of the...
Cholinergic Fibers Image Interpretation, Computer-Assisted White Matter Cholinergic Neurons Leukoencephalopathies Magnetic Resonance Imaging Predictive Value of Tests Cerebrovascular Disorders Humans Algorithms Female Reproducibility of Results Middle Aged Deep Learning Cognitive Dysfunction Cognition Aged Aged, 80 and over Male
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Topology aware multitask cascaded U-Net for cerebrovascular segmentation.

PloS one Dec 5, 2024
Cerebrovascular segmentation is a crucial preliminary task for many computer-aided diagnosis tools dealing with cerebrovascular pathologies. Over the last years, deep learning based methods have been widely applied to this task. However, classic deep...
Magnetic Resonance Angiography Brain Deep Learning Image Processing, Computer-Assisted Cerebrovascular Disorders Humans Neural Networks, Computer Algorithms
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Automated Cerebrovascular Segmentation and Visualization of Intracranial Time-of-Flight Magnetic Resonance Angiography Based on Deep Learning.

Journal of imaging informatics in medicine Aug 12, 2024
Time-of-flight magnetic resonance angiography (TOF-MRA) is a non-contrast technique used to visualize neurovascular. However, manual reconstruction of the volume render (VR) by radiologists is time-consuming and labor-intensive. Deep learning-based (...
Cerebrovascular Disorders Magnetic Resonance Angiography Cerebral Arteries Humans Retrospective Studies Aged Middle Aged Deep Learning Image Processing, Computer-Assisted
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