Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia
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Purpose: To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia. Methods: This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017–March 2018, n = 596) and internal validation test set (April 2018–June 2018, n = 204). Results: Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively. Conclusion: Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.
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DL-based WMH volumetry effectively stratifies subcortical vascular dementia severity.
“DL-based WMH volumetry effectively stratifies subcortical vascular dementia severity”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Joo et al., 2022
The Results sections on Fazekas-scale classification and differentiating subcortical vascular dementia show that DL-based WMH volumetry classifies WMH grades and distinguishes patients with subcortical vascular dementia from others. They do not show that it stratifies the severity of dementia among affected patients; WMH grade is not presented as a dementia-severity grade. (LLM, conf=0.87)
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- Authors
- Joo, L., Shim, W. H., Suh, C. H., Lim, S. J., Heo, H., Kim, W. S., Hong, E., Lee, D., Sung, J., Lim, J.-S., Lee, J.-H., & Kim, S. J.
- Affiliations
- Korea University Guro Hospital (KR)
- Asan Medical Center (KR)
- Corresponding author
- Suh, Chong Hyun
- ORCID
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-09-15
- Volume
- 17
- Issue
- 9
- Pages
- e0274562
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC9477348
- OpenAlex
- W4295898323
- Cited by
- 35
- References
- 46
- Citations by year
- 2026: 12 · 2025: 9 · 2024: 7 · 2023: 6 · 2022: 1
- FWCI
- 3.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (NRF- 350 2021R1C1C1014413)
- Topics
- Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Keywords
- white matter hyperintensities, automatic segmentation, Fazekas scale, subcortical vascular dementia, deep learning, convolutional neural networks, FLAIR MRI, WMH volume, dementia classification, diagnostic accuracy
- MeSH terms
- Deep Learning, Humans, Magnetic Resonance Imaging, Retrospective Studies, Dementia, Vascular, Leukoaraiosis, White Matter
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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