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Research ArticleNEURODEGENERATIVE DISORDER IMAGING

Automated Quantification of Cerebral Microbleeds in SWI: Association with Vascular Risk Factors, White Matter Hyperintensity Burden, and Cognitive Function

Ji Su Ko, Yangsean Choi, Eun Seon Jeong, Hyun-Jung Kim, Grace Yoojin Lee, Ji Eun Park, Namkug Kim and Ho Sung Kim
American Journal of Neuroradiology March 2025, DOI: https://doi.org/10.3174/ajnr.A8552
Ji Su Ko
aFrom the Department of Radiology and Research Institute of Radiology (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea
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Yangsean Choi
aFrom the Department of Radiology and Research Institute of Radiology (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea
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Eun Seon Jeong
aFrom the Department of Radiology and Research Institute of Radiology (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea
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Hyun-Jung Kim
bDepartment of Convergence Medicine (H.-J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
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Grace Yoojin Lee
bDepartment of Convergence Medicine (H.-J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
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Ji Eun Park
aFrom the Department of Radiology and Research Institute of Radiology (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea
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Namkug Kim
bDepartment of Convergence Medicine (H.-J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
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Ho Sung Kim
aFrom the Department of Radiology and Research Institute of Radiology (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea
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Abstract

BACKGROUND AND PURPOSE: The amount and distribution of cerebral microbleeds (CMB) are important risk factors for cognitive impairment. Our objective was to train and validate a deep learning (DL)-based segmentation model for cerebral microbleeds (CMBs) on SWI and to find associations among CMB, cognitive impairment, and vascular risk factors.

MATERIALS AND METHODS: Participants in this single-institution retrospective study underwent brain MRI to evaluate cognitive impairment between January and September 2023. For training the DL model, the nnU-Net framework was used without modifications. The performance of the DL model was evaluated on independent internal and external validation data sets. Linear regression analysis was used to find associations among log-transformed CMB numbers, cognitive function (Mini-Mental Status Examination [MMSE]), white matter hyperintensity (WMH) burden, and clinical vascular risk factors (age, sex, hypertension, diabetes, lipid profiles, and body mass index).

RESULTS: Training of the DL model (n = 287) resulted in a robust segmentation performance with an average Dice score of 0.73 (95% CI, 0.67–0.79) in an internal validation set (n = 67) and modest performance in an external validation set (Dice score = 0.46; 95% CI, 0.33–0.59; n = 68). In a temporally independent clinical data set (n = 448), older age, hypertension, and WMH burden were significantly associated with CMB numbers in all distributions (total, lobar, deep, and cerebellar; all P < . 01). The MMSE was significantly associated with hyperlipidemia (β = 1.88; 95% CI, 0.96–2.81; P < . 001), WMH burden (β = −0.17 per 1% WMH burden, 95% CI, −0.27–0.08; P < . 001), and total CMB number (β = −0.01 per 1 CMB, 95% CI, −0.02–0.001; P = .04) after adjusting for age and sex.

CONCLUSIONS: The DL model showed a robust segmentation performance for CMB. In all distributions, CMB had significant positive associations with WMH burden. Increased WMH burden and CMB numbers were associated with decreased cognitive function.

ABBREVIATIONS:

CMB
cerebral microbleed
DL
deep learning
DSC
Dice similarity coefficient
HDL
high-density lipoprotein
LDL
low-density lipoprotein
MMSE
Mini-Mental Status Examination
nnU-Net
no-new-Net
SVD
small-vessel disease
WMH
white matter hyperintensity

Footnotes

  • This work was supported by a grant (2024IP0026-1) from the Asan Institute for Life Sciences, Asan Medical Center, Seoul, Korea.

  • Disclosure forms provided by the authors are available with the full text and PDF of this article at www.ajnr.org.

  • © 2025 by American Journal of Neuroradiology
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Cite this article
Ji Su Ko, Yangsean Choi, Eun Seon Jeong, Hyun-Jung Kim, Grace Yoojin Lee, Ji Eun Park, Namkug Kim, Ho Sung Kim
Automated Quantification of Cerebral Microbleeds in SWI: Association with Vascular Risk Factors, White Matter Hyperintensity Burden, and Cognitive Function
American Journal of Neuroradiology Mar 2025, DOI: 10.3174/ajnr.A8552

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Automated Microbleed Detection and Cognitive Links
Ji Su Ko, Yangsean Choi, Eun Seon Jeong, Hyun-Jung Kim, Grace Yoojin Lee, Ji Eun Park, Namkug Kim, Ho Sung Kim
American Journal of Neuroradiology Mar 2025, DOI: 10.3174/ajnr.A8552
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