ArticleNPJ digital medicine2025
Deep learning-based brain age predicts stroke recurrence in acute ischemic cerebrovascular disease.
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 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Safety and efficacy of repeated intravenous thrombolysis within 3 months for acute ischemic stroke: a systematic review and individual-patient data meta-analysis.Journal of neurology · 2026Pooled it
- Multimorbidity burden and patterns associated with DeepBrainNet-derived brain-age gap in dementia-free older adults: A community-based study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Infarct volume versus NIHSS score for ischemic and bleeding risk stratification after acute ischemic stroke.Military Medical Research · 2026Article
- Prediction of glioma-related epilepsy by Brain Age Index: a multicenter study.Frontiers in neuroscience · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
Abstract
Acute ischemic cerebrovascular disease (AICVD) exhibits high recurrence rates, necessitating novel biomarkers for refined risk stratification. While MRI-derived brain age correlates with stroke incidence, its prognostic utility for recurrence is unestablished. We developed the Mask-based Brain Age estimation Network (MBA Net), a deep learning framework designed for AICVD patients. MBA Net predicts contextual brain age (CBA) in non-infarcted regions by masking acute infarcts on T2-FLAIR images, thereby mitigating the confounding effects of dynamic infarcts during acute-phase neuroimaging. The model was trained on data from 5353 healthy individuals and then applied to a multicenter cohort of 10,890 AICVD patients. Brain age gap (BAG), defined as the deviation between CBA and chronological age, independently predicted stroke recurrence at both 3 months and 5 years, outperforming chronological age. Incorporating BAG into established prediction models significantly improved discriminative performance. These findings support brain age's potential utility in AI-driven precision strategies for secondary stroke prevention.
Identifiers
What OpenQuestion holds
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.