ArticleScientific reports2023
Head CT deep learning model is highly accurate for early infarct estimation.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep-learning-based non-contrast CT for detecting acute ischemic stroke: a systematic review and HSROC meta-analysis of patient-level diagnostic accuracy.BMC neurology · 2025Pooled it
- Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.Radiology · 2025Pooled it
- Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.Clinical neuroradiology · 2026Article
- Health system learning enables generalist neuroimaging models.Nature medicine · 2026Article
- Robust slice-level stroke classification in non-contrast head CT via structural consistency regularization and counterfactual suppression.Frontiers in bioengineering and biotechnology · 2026Article
- Evaluation of ChatGPT-5 for Automated ASPECTS Assessment on Non-Contrast CT in Acute Ischemic Stroke.Diagnostics (Basel, Switzerland) · 2025Article
- Health system learning achieves generalist neuroimaging models.Research square · 2025Article
- Estimation of Acute Infarct Core and Hypoperfused Region from Baseline Noncontrast Computed Tomography and Computed Tomography Angiography Scans of Patients with Ischemic Stroke.Stroke (Hoboken, N.J.) · 2025Article
- Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests.Scientific reports · 2025Article
- The Relationship Between Clinical Features of Ischemic Stroke and miRNA Expression in Stroke Patients: A Systematic Review.Neurology international · 2025Review
- Acute Infarct Core Volume Estimation on Noncontrast Computed Tomography With a Deep Learning Algorithm.Stroke (Hoboken, N.J.) · 2025Article
- Deep learning biomarker of chronometric and biological ischemic stroke lesion age from unenhanced CT.NPJ digital medicine · 2024Article
- APIS: a paired CT-MRI dataset for ischemic stroke segmentation - methods and challenges.Scientific reports · 2024Article
- Random expert sampling for deep learning segmentation of acute ischemic stroke on non-contrast CT.Journal of neurointerventional surgery · 2024Article
- Review
- Incorporating algorithmic uncertainty into a clinical machine deep learning algorithm for urgent head CTs.PloS one · 2023Article
- A retrospective analysis comparing AIDoc and RAPIDAI in the detection of large vessel occlusions.Brain circulationArticle
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6 h) acute infarct identification. We developed a deep learning model that detects and delineates suspected early acute infarcts on NCCT, using diffusion MRI as ground truth (3566 NCCT/MRI training patient pairs). The model substantially outperformed 3 expert neuroradiologists on a test set of 150 CT scans of patients who were potential candidates for thrombectomy (60 stroke-negative, 90 stroke-positive middle cerebral artery territory only infarcts), with sensitivity 96% (specificity 72%) for the model versus 61-66% (specificity 90-92%) for the experts; model infarct volume estimates also strongly correlated with those of diffusion MRI (r
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