ArticleNPJ digital medicine2025
Deep adaptive learning predicts and diagnoses CSVD-related cognitive decline using radiomics from T
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed.
- Early identification of vascular cognitive impairment from a multimodal perspective: a combined diagnosis from targeted cognitive assessments, imaging biomarkers, and molecular fluid biomarkers to ecological behavioral characteristics.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- A stacked multi-classifier for multi-modal data fusion in transcranial sonography-based Parkinson's disease assessment.NPJ Parkinson's disease · 2026Article
- Transformer-based prediction of radiotherapy couch shift risk in prostate cancer based on rectal volume.Scientific reports · 2026Article
- Interpretable machine learning for cognitive impairment assessment: integration of clinical and radiomic white matter hyperintensities features.Journal of translational medicine · 2026Article
- An Integrated QSM-Radiomics Nomogram With Clinical and Imaging Markers for Stratifying Cognitive Impairment in Hypertension.CNS neuroscience & therapeutics · 2026Article
- Development and internal validation of a spatiotemporal gait parameter-based diagnostic model for cerebral small vessel disease.Frontiers in aging neuroscience · 2026Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Early identification of cerebral small vessel disease related cognitive impairment (CSVD-CI) is crucial for timely clinical intervention. We developed a Transformer-based deep learning model using white matter hyperintensity (WMH) radiomics features from T
Identifiers
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Registered trials
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.