ArticleNeurological research and practice2026
AI-assisted hemorrhage detection following endovascular stroke treatment: a retrospective diagnostic accuracy study.
Article in Neurological research and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAntithrombotic therapy is essential for preventing strokes, but its use after reperfusion therapy requires careful monitoring due to the risk of hemorrhagic transformation. Non-contrast-enhanced computed tomography (NCCT) is the standard for detecting intracranial hemorrhages post-stroke. Artificial intelligence may enhance hemorrhage detection and improve patient safety. This study evaluates AI’s sensitivity and specificity in detecting hemorrhagic events in NCCT scans within 48 h after endovascular stroke treatment, compared to standard radiological assessment.
methodsA retrospective, single-center study was conducted at a European stroke center, including 495 NCCT scans from 425 patients who underwent endovascular stroke treatment between 08/2021 and 06/2024. A CE-marked AI software based on convolutional neural networks (CNN) analyzed the scans independently. The reference standard was assessments of two board-certified neuroradiologists, and AI results were compared with routine radiological reports. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated, and inter-rater reliability was assessed using Cohen’s kappa.
resultsThe reference standard identified hemorrhages in 197 NCCT scans. The AI system showed sensitivity of 95.9%, specificity of 84.6%, PPV of 80.4%, and NPV of 96.9%. Radiological reports had sensitivity of 91.9%, specificity of 96.3%, PPV of 94.3%, and NPV of 94.7%. Cohen’s kappa was higher for radiological reports (0.886) than AI (0.780), indicating stronger agreement with the reference standard. AI had a higher false-positive rate (15.4%) than radiological reports (3.7%).
conclusionsAI demonstrated high sensitivity for detecting intracranial hemorrhages but had a higher false-positive rate compared to routine radiological assessment. While AI can aid clinical decision-making, radiologists show superior overall diagnostic accuracy. Further research is needed to explore the impact of AI-assisted decision-making on stroke management and secondary prevention.
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