Deep learning-based prediction of cerebral white matter hyperintensity burden using carotid magnetic resonance angiography

Carotid TOF MRA alone can serve as a predictive marker for WMH burden when analyzed using deep learning. This approach highlights the potential utility of extracranial carotid imaging as a non-invasive surrogate for early and accessible assessment of cerebrovascular risk.

Authors

Kyu Sung Choi (ORCID: https://orcid.org/0000-0002-5175-3307)
Taehoon Shin (ORCID: https://orcid.org/0000-0003-1780-573X)
Inpyeong Hwang (ORCID: https://orcid.org/0000-0002-1291-8973)
Seung Hong Choi (ORCID: https://orcid.org/0000-0002-0412-2270)
Jiho Lee (ORCID: https://orcid.org/0000-0001-8027-835X)

Institutions

Ewha Womans University (KR)
Seoul National University (KR)
Seoul National University Hospital (KR)

Publication Details

Journal: Frontiers in Neurology
Published: 2025-09-09
DOI: https://doi.org/10.3389/fneur.2025.1656705
Citations: 1
Primary Topic: Brain Tumor Detection and Classification
Type: article
Field-Weighted Citation Impact: 0.84

Funders

National Research Foundation
National Research Foundation of Korea
Ministry of Science and ICT, South Korea
Institute for Information and Communications Technology Promotion

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article

Deep learning-based prediction of cerebral white matter hyperintensity burden using carotid magnetic resonance angiography

Kyu Sung Choi, Taehoon Shin, Inpyeong Hwang, Seung Hong Choi et al.

Sep 9, 2025

1 citations

Frontiers in Neurology

Brain Tumor Detection and Classification

0.84

article

Deep learning-based prediction of cerebral white matter hyperintensity burden using carotid magnetic resonance angiography

Kyu Sung Choi, Taehoon Shin, Inpyeong Hwang, Seung Hong Choi, Jiho Lee

September 9, 2025article en

1 citations

Abstract

Carotid TOF MRA alone can serve as a predictive marker for WMH burden when analyzed using deep learning. This approach highlights the potential utility of extracranial carotid imaging as a non-invasive surrogate for early and accessible assessment of cerebrovascular risk.

Frontiers in NeurologyVol. 16

Ewha Womans University (KR), Seoul National University (KR), Seoul National University Hospital (KR)

National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion

Openalex Percentile: Top 22%

Brain Tumor Detection and Classification

0.84

DOI Open Access PDF OpenAlex

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Deep learning-based prediction of cerebral white matter hyperintensity burden using carotid magnetic resonance angiography — Kyu Sung Choi, Taehoon Shin, et al. · Frontiers in Neurology (2025) | TGRS Research Map | TGRS