Evidence map›Paper›PMID 39443150›Full record

ArticleAJNR. American journal of neuroradiology2025

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, Ho Sung Kim

Abstract read
In one paragraph

Article in AJNR. American journal of neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ji Su KoFrom 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.ORCID 0000-0001-6589-2431
Yangsean ChoiFrom 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 phillipchoi007@gmail.com.ORCID 0000-0003-1674-7101
Eun Seon JeongFrom 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.
Hyun-Jung KimDepartment of Convergence Medicine (H.-J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
Grace Yoojin LeeDepartment of Convergence Medicine (H.-J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.ORCID 0009-0007-5600-0834
Ji Eun ParkFrom 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.ORCID 0000-0002-4419-4682
Namkug KimDepartment of Convergence Medicine (H.-J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.ORCID 0000-0002-3438-2217
Ho Sung KimFrom 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.ORCID 0000-0002-9477-7421

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeThe 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

methodsParticipants 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).

resultsTraining of the DL model (

conclusionsThe 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.

Indexed as

Cerebral HemorrhageCognitive DysfunctionDeep LearningMagnetic Resonance ImagingWhite MatterAgedAged, 80 and overCognitionFemaleHumansMaleMiddle AgedRetrospective StudiesRisk Factors

Identifiers

PMID39443150
PMCPMC12091992

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.