Application of Artificial Intelligence-based Magnetic Resonance Imaging in Diagnosis of Cerebral Small Vessel Disease

Overview

Journal CNS Neurosci Ther

Specialties Neurology
Pharmacology

Date 2024 Jul 24

PMID 39045778

Authors

Xiaofei Hu

Li Liu

Ming Xiong

Jie Lu

Affiliations

Soon will be listed here.

Abstract

Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. With the development of artificial intelligence technology based on deep learning, the extraction of high-dimensional features in imaging can assist doctors in clinical decision-making, and it has been widely used in brain function and mental disorders, and cardiovascular and cerebrovascular diseases. This paper summarizes the global research results in recent years and briefly describes the application of deep learning in evaluating CSVD signs in MRI imaging, including recent small subcortical infarcts, lacunes of presumed vascular origin, vascular white matter hyperintensity, enlarged perivascular spaces, cerebral microbleeds, brain atrophy, cortical superficial siderosis, and cortical cerebral microinfarct.

Citing Articles

Application of artificial intelligence-based magnetic resonance imaging in diagnosis of cerebral small vessel disease.

Hu X, Liu L, Xiong M, Lu J CNS Neurosci Ther. 2024; 30(7):e14841.

PMID: 39045778 PMC: 11267174. DOI: 10.1111/cns.14841.

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