ArticleDiagnostics (Basel, Switzerland)2023
Deep Learning-Based Extraction of Biomarkers for the Prediction of the Functional Outcome of Ischemic Stroke Patients.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Mechanisms and integrative machine learning approaches to blood-brain barrier biomarker profiling for personalized ischemic stroke management.Physiological reports · 2026Review
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Integration of imaging and clinical biomarkers for cerebral infarction diagnosis via NeuroFusionNet.Frontiers in neuroscience · 2026Article
- Automatic prediction of stroke treatment outcomes: latest advances and perspectives.Biomedical engineering letters · 2025Review
- Ischemic stroke outcome prediction with diversity features from whole brain tissue using deep learning network.Frontiers in neurology · 2024Article
- Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke.Frontiers in artificial intelligence · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Accurately predicting functional outcomes in stroke patients remains challenging yet clinically relevant. While brain CTs provide prognostic information, their practical value for outcome prediction is unclear. We analyzed a multi-center cohort of 743 ischemic stroke patients (<72 h onset), including their admission brain NCCT and CTA scans as well as their clinical data. Our goal was to predict the patients' future functional outcome, measured by the 3-month post-stroke modified Rankin Scale (mRS), dichotomized into good (mRS ≤ 2) and poor (mRS > 2). To this end, we developed deep learning models to predict the outcome from CT data only, and models that incorporate other patient variables. Three deep learning architectures were tested in the image-only prediction, achieving 0.779 ± 0.005 AUC. In addition, we created a model fusing imaging and tabular data by feeding the output of a deep learning model trained to detect occlusions on CT angiograms into our prediction framework, which achieved an AUC of 0.806 ± 0.082. These findings highlight how further refinement of prognostic models incorporating both image biomarkers and clinical data could enable more accurate outcome prediction for ischemic stroke patients.
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Registered trials
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