ArticleAmerican journal of Alzheimer's disease and other dementias
Automatic Detection of Cognitive Impairment in Patients With White Matter Hyperintensity Using Deep Learning and Radiomics.
Article in American journal of Alzheimer's disease and other dementias. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development and validation of a deep learning-based automatic segmentation and classification of cerebral white matter hyperintensities.European radiology · 2026Article
- Artificial intelligence empowers the upgrading of intelligent quantitative assessment for white matter hyperintensities: opportunities and challenges.European radiology · 2026Article
- Research progress in predicting the conversion from mild cognitive impairment to Alzheimer's disease via multimodal MRI and artificial intelligence.Frontiers in neurology · 2025Review
- Research progress on predicting KRAS gene mutations in colorectal cancer by combining radiomics and multimodal medical imaging.Frontiers in oncology · 2025Review
- Association between white matter structural damage and cognitive impairment in patients with cerebral small vessel disease based on TBSS technology.Frontiers in neurology · 2025Article
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9 authors.
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Abstract
White matter hyperintensity (WMH) is associated with cognitive impairment. In this study, 79 patients with WMH from hospital 1 were randomly divided into a training set (62 patients) and an internal validation set (17 patients). In addition, 29 WMH patients from hospital 2 were used as an external validation set. Cognitive status was determined based on neuropsychological assessment results. A deep learning convolutional neural network of VB-Nets was used to automatically identify and segment whole-brain subregions and WMH. The PyRadiomics package in Python was used to automatically extract radiomic features from the WMH and bilateral hippocampi. Delong tests revealed that the random forest model based on combined features had the best performance for the detection of cognitive impairment in WMH patients, with an AUC of 0.900 in the external validation set. Our results provide clinical doctors with a reliable tool for the early diagnosis of cognitive impairment in WMH patients.
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