AIMC Topic: Moyamoya Disease
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Machine learning identifies KRT8 dysregulation and endothelial remodeling in Moyamoya disease.
Scientific reports
Jul 7, 2025
Moyamoya disease (MMD) is a rare occlusive cerebrovascular disease, and its pathological mechanism remains unclear at present. The abnormal vascular remodeling may be involved in vascular endothelial cells. In this study, RNA seq was performed on the...
Deep-learning tool for early identification of non-traumatic intracranial hemorrhage etiology and application in clinical diagnostics based on computed tomography (CT) scans.
PeerJ
Feb 26, 2025
BACKGROUND: To develop an artificial intelligence system that can accurately identify acute non-traumatic intracranial hemorrhage (ICH) etiology (aneurysms, hypertensive hemorrhage, arteriovenous malformation (AVM), Moyamoya disease (MMD), cavernous ...
Combining machine learning with external validation to explore necroptosis and immune response in moyamoya disease.
BMC immunology
Feb 13, 2025
Moyamoya disease (MMD) is a rare chronic vascular disease leads to cognitive impairment and stroke with its etiology unknown. The relationship between necroptosis or necroinflammation and MMD pathogenesis was poorly understood. Differentially express...
Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Dec 31, 2024
Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and intervention can significantly reduce the risk of new-onset stroke in patients with MMD. However, t...
TdCCA with Dual-Modal Signal Fusion: Degenerated Occipital and Frontal Connectivity of Adult Moyamoya Disease for Early Identification.
Translational stroke research
Dec 5, 2024
Cognitive impairment in patients with moyamoya disease (MMD) manifests earlier than clinical symptoms. Early identification of brain connectivity changes is essential for uncovering the pathogenesis of cognitive impairment in MMD. We proposed a tempo...
Aneurysmal formation of periventricular anastomosis is associated with collateral development of Moyamoya disease and its rupture portends poor prognosis: detailed analysis by multivariate statistical and machine learning approaches.
Neurosurgical review
Nov 19, 2024
Periventricular anastomosis (PA) is the characteristic collateral network in Moyamoya disease (MMD). However, PA aneurysms are rare, resulting in limited knowledge of their clinical significance. We aimed to elucidate the associated factors and clini...
A correspondence of evaluation of deep learning algorithms in detecting Moyamoya disease: a systematic review and single-arm meta-analysis.
Neurosurgical review
Aug 21, 2024
Multi-parameter MRI-Based Machine Learning Model to Evaluate the Efficacy of STA-MCA Bypass Surgery for Moyamoya Disease: A Pilot Study.
Journal of imaging informatics in medicine
Jul 17, 2024
Superficial temporal artery-middle cerebral artery (STA-MCA) bypass surgery represents the primary treatment for Moyamoya disease (MMD), with its efficacy contingent upon collateral vessel development. This study aimed to develop and validate a machi...
Evaluation of deep learning algorithms in detecting moyamoya disease: a systematic review and single-arm meta-analysis.
Neurosurgical review
Jun 29, 2024
The diagnosis of Moyamoya disease (MMD) relies heavily on imaging, which could benefit from standardized machine learning tools. This study aims to evaluate the diagnostic efficacy of deep learning (DL) algorithms for MMD by analyzing sensitivity, sp...
Machine learning model for predicting stroke recurrence in adult stroke patients with moyamoya disease and factors of stroke recurrence.
Clinical neurology and neurosurgery
Apr 29, 2024
OBJECT: The aim of this study was at building an effective machine learning model to contribute to the prediction of stroke recurrence in adult stroke patients subjected to moyamoya disease (MMD), while at analyzing the factors for stroke recurrence.
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