Observational studyPloS one2022
Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia.
Observational study in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning applications in vascular neuroimaging for the diagnosis and prognosis of cognitive impairment and dementia: a systematic review and meta-analysis.Alzheimer's research & therapy · 2025Pooled it
- Factors associated with symptom relief and lesion formation in acute ischemic stroke and transient ischemic attack.BMC neurology · 2026Article
- Two distinct patterns of progression of sporadic cerebral small vessel disease.International journal of stroke : official journal of the International Stroke Society · 2026Article
- Development and validation of a deep learning-based automatic segmentation and classification of cerebral white matter hyperintensities.European radiology · 2026Article
- Correlation analysis of serum 25(OH)D levels with white matter hyperintensities and gait disorders in patients with cerebral small vessel disease.Scientific reports · 2026Article
- Sex-Based Differences in Disease Burden and Phenotype in CADASIL: A Multicenter Study of 368 Korean Patients.Neurology. Genetics · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Automated quantification of the Alberta Stroke Programme Early CT Score (ASPECTS) and infarct core on diffusion-weighted imaging in acute ischaemic stroke: multi-centre validation and severity stratification.Brain communications · 2026Article
- Advantage of grading classification using volumetric artificial intelligence for periventricular hyperintensity and deep subcortical white matter hyperintensity.Scientific reports · 2025Article
- Association of Intracranial Plaque Features with the Severity of White Matter Hyperintensities in Middle-Aged and Older Community-Dwelling Adults.Biomedicines · 2025Article
- Automated rating of Fazekas scale in fluid-attenuated inversion recovery MRI for ischemic stroke or transient ischemic attack using machine learning.Scientific reports · 2025Article
- Generalizability of trial criteria on amyloid-lowering therapy against Alzheimer's disease to individuals with mild cognitive impairment or early Alzheimer's disease in the general population.European journal of epidemiology · 2025Article
- Orthostatic hypotension is involved in cognitive impairment in patients with multiple system atrophy: a multi-center cohort study in China.Journal of neurology · 2025Observational
- Modulation of the Association Between Corticospinal Tract Damage and Outcome After Stroke by White Matter Hyperintensities.Neurology · 2024Article
- Artificial intelligence for volumetric measurement of cerebral white matter hyperintensities on thick-slice fluid-attenuated inversion recovery (FLAIR) magnetic resonance images from multiple centers.Scientific reports · 2024Article
- Automated brain segmentation and volumetry in dementia diagnostics: a narrative review with emphasis on FreeSurfer.Frontiers in aging neuroscience · 2024Review
- Impact of white matter hyperintensity volumes estimated by automated methods using deep learning on stroke outcomes in small vessel occlusion stroke.Frontiers in aging neuroscience · 2024Article
- Under pressure: the interplay of hypertension and white matter hyperintensities with cognition in chronic stroke aphasia.Brain communications · 2024Article
- White matter hyperintensities modify relationships between corticospinal tract damage and motor outcomes after stroke.medRxiv : the preprint server for health sciences · 2023Article
- Characterization of MRI White Matter Signal Abnormalities in the Pediatric Population.Children (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo 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.
methodsThis 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).
resultsOptimal 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.
conclusionDeep 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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