ArticleBJR artificial intelligence2025
Artificial intelligence assisted detection of large vessel occlusion on CT angiography in acute stroke patients: a multi-reader multi-case study.
Article in BJR artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Using a Deep Learning-Based Decision Support System to Predict Emergent Large Vessel Occlusion Using Non-Contrast Computed Tomography.Journal of clinical medicine · 2025Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: We assessed the impact of artificial intelligence (AI) software (e-CTA, Brainomix) on clinical decision-making in patients with suspected acute ischemic stroke. Methods: A retrospective, multi-reader-multi-case crossover design compared readers' performance with vs without software support. Twenty cases were included, 10 with large vessel occlusion (LVO) and 10 without LVO. Twenty-one NHS clinicians, representing intended software users ranging in experience, conducted 2 sessions (washout period > 2 weeks). In session 1, software support was provided for 10 randomly selected cases. In session 2, support allocation was reversed. Outcome measures included LVO detection, collateral scoring, diagnosis, treatment decision, time taken and confidence. Results: Sensitivity, specificity, and accuracy of LVO detection improved with imaging software for LVO detection, with increased confidence and reduced time taken. There was no significant difference in collateral scoring or diagnoses. Conclusion: e-CTA can improve performance of NHS clinicians when interpreting acute stroke imaging. Advances in knowledge: This paper provides new evidence that AI decision support software has the capacity to improve the performance of representative users in the NHS when interpreting imaging to identify patients for acute stroke treatments.
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Registered trials
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