AI Tool Predicts Stroke Recovery by Detecting Seizures in Brain Signals
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AI algorithm applied to EEG recordings can help predict functional outcomes after stroke by measuring seizure burden. Higher seizure burden correlated with poorer outcomes.
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Automated EEG interpretation can identify seizures and quantify seizure burden more efficiently than manual review which is time-consuming and subject to human error.
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High seizure burden (≥ 50%) was associated with 4 times higher odds of poor functional outcome, especially if occurring late in the recording.
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Study suggests automated EEG analysis could help guide treatment decisions and improve patient care by detecting problematic seizure activity.
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Further research needed to validate machine learning algorithms for guiding physician decision-making and treating neurologic conditions.