ArticleFrontiers in neuroscience2025
Automated ischemic stroke lesion detection on non-contrast brain CT: a large-scale clinical feasibility test AI stroke lesion detection on NCCT.
Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Defining the Therapeutic Ceiling of Endovascular Thrombectomy in Large-Core Stroke: Beyond the Limits of ASPECTS.Stroke · 2026Article
- Early Diagnosis of Ischemic Stroke on Non-Contrast CT Scans Using a Convolutional Neural Network: A Case Study from Mulago National Referral Hospital, Uganda.Research square · 2026Article
- Infarct volume versus NIHSS score for ischemic and bleeding risk stratification after acute ischemic stroke.Military Medical Research · 2026Article
- Parameter-free dual-student ensemble for semi-supervised ischemic stroke segmentation on follow-up NCCT.Frontiers in neurology · 2026Article
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12 authors.
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Abstract
Background: Non-contrast CT (NCCT) is widely used imaging modality for acute stroke imaging but often fails to detect subtle early ischemic changes. Such underestimation can lead clinicians to overlook tissue-level information. This study aimed to develop and externally validate automated software for detecting ischemic lesions on NCCT and to assess its clinical feasibility in stroke patients undergoing endovascular thrombectomy. Methods: In this retrospective, multicenter cohort study (May 2011-April 2024), a modified 3D U-Net model was trained using paired NCCT and diffusion-weighted imaging (DWI) data from 2,214 patients with acute ischemic stroke. External validation was performed in 458 subjects. Clinical feasibility was assessed in 603 endovascular thrombectomy-treated patients with complete recanalization. Model outputs were compared against expert-annotated DWI lesions for sensitivity, specificity, and volumetric correlation. Clinical endpoints included follow-up DWI lesion volumes, hemorrhagic transformation, and 3-month modified Rankin Scale outcomes. Results: A total of 458 subjects were evaluated for external validation (mean age, 64 years ± 16; 265 men). The model achieved 75.3% sensitivity (95% CI, 70.9-79.9%) and 79.1% specificity (95% CI, 77.1-81.3%). In the feasibility cohort ( Conclusion: The automated NCCT-based lesion detection model demonstrated reliable diagnostic performance and provided clinically relevant prognostic information in endovascular thrombectomy-treated stroke patients.
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