Artificial Intelligence-Based Differentiation of Ischemic and Hemorrhagic Stroke: A Retrospective, Analytical and Observational Case-Control Design
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: Rapid differentiation between ischemic and hemorrhagic stroke is essential because management differs significantly. In settings without immediate neuroimaging, artificial intelligence (AI) may assist early decision-making. To evaluate the performance of AI in distinguishing ischemic from hemorrhagic stroke using history and physical examination alone. Methods: This retrospective, observational, case-control study included 54 adults (27 ischemic, 27 hemorrhagic) with neuroimaging-confirmed stroke in a tertiary hospital from January to June 2025. De-identified clinical data were entered into a structured AI prompt. AI predictions were compared with imaging diagnoses, and accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Results: Ischemic stroke patients were slightly older than hemorrhagic cases; hypertension was the most frequent comorbidity. One-sided weakness was the most common presenting symptom, while headache occurred only in hemorrhagic stroke. Overall accuracy of Google Gemini 2.5 Pro was 66.7% (p = 0.0099). Sensitivity for ischemic stroke was 85.2%, specificity for hemorrhagic stroke was 48.1%, and PPV and NPV were 62.2% and 76.5%, respectively. Conclusion: AI demonstrated moderate diagnostic performance, with greater accuracy for ischemic than hemorrhagic stroke. AI may serve as an adjunct in resource-limited settings but should not replace neuroimaging. Larger studies and model refinement are recommended.
Source: Brain Science Advances
Language: English
Subfield: Clinical Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7208719491