AIMC Topic: Intracranial Arteriosclerosis

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Intracranial stenosis prediction using a small set of risk factors in the Tromsø Study.

BMC medical informatics and decision making Feb 20, 2025
Intracranial atherosclerotic stenosis (ICAS) refers to a narrowing of intracranial arteries due to plaque buildup on the inside of the vessel walls restricting blood flow. Early detection of ICAS is crucial to prevent serious consequences such as str...
Male Middle Aged Aged Norway Intracranial Arteriosclerosis Magnetic Resonance Angiography Constriction, Pathologic Machine Learning Humans Risk Factors Female
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Patch-Wise Deep Learning Method for Intracranial Stenosis and Aneurysm Detection-the Tromsø Study.

Neuroinformatics Jan 15, 2025
Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in the cerebrovascular system. ICAS causes a narrowing of the arterial lumen, thereby restricting blood flow, while aneurysms involve the ballooning of b...
Aged Male Female Image Processing, Computer-Assisted Magnetic Resonance Angiography Deep Learning Middle Aged Humans Intracranial Aneurysm Intracranial Arteriosclerosis Norway
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Deep Learning-Based Approach for the Diagnosis of Moyamoya Disease.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Sep 25, 2020
OBJECTIVES: Moyamoya disease is a unique cerebrovascular disorder that is characterized by chronic bilateral stenosis of the internal carotid arteries and by the formation of an abnormal vascular network called moyamoya vessels. In this stury, the au...
Moyamoya Disease Intracranial Arteriosclerosis Predictive Value of Tests Female Diagnosis, Differential Image Interpretation, Computer-Assisted Young Adult Deep Learning Humans Reproducibility of Results Diagnosis, Computer-Assisted Retrospective Studies Male Magnetic Resonance Imaging Adult Middle Aged Adolescent
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Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.

IEEE transactions on bio-medical engineering Feb 1, 2019
OBJECTIVE: To develop an automated vessel wall segmentation method using convolutional neural networks to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intracranial atherosclerotic disease (ICAD).
Imaging, Three-Dimensional Contrast Media Middle Aged Magnetic Resonance Angiography Middle Cerebral Artery Automation Female Neural Networks, Computer Image Interpretation, Computer-Assisted Intracranial Arteriosclerosis Male Retrospective Studies Risk Factors Humans
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