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StrokeTimer uses AI to estimate ischemic stroke onset time

By PulseAugur Editorial · [2 sources] ·

Researchers have developed StrokeTimer, a new framework designed to estimate the onset time of ischemic strokes using non-contrast CT scans. This method employs self-supervised disentanglement and energy-guided contrastive learning to identify subtle indicators of stroke, even with imbalanced data and varying scanner types. StrokeTimer achieved a macro AUC of 0.69 and a macro F1-score of 0.57 on a multi-center dataset, significantly outperforming existing baseline approaches. AI

IMPACT Potential to improve treatment decisions for acute ischemic stroke by providing more accurate onset-time estimations.

RANK_REASON The cluster contains a research paper detailing a new AI model for a specific medical application.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

StrokeTimer uses AI to estimate ischemic stroke onset time

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Weiru Wang, Susanne G. H. Olthuis, Elizaveta Lavrova, Robert J. van Oostenbrugge, Charles B. L. M. Majoie, Wim H. van Zwam, Ruisheng Su ·

    StrokeTimer: Robust Representation Learning for Ischemic Stroke Onset-Time Estimation from Non-contrast CT

    arXiv:2606.04722v1 Announce Type: new Abstract: Ischemic stroke is a major global disease. Treatment decisions are highly time-sensitive, as eligibility for reperfusion therapies relies on the interval between stroke onset and intervention. However, the true onset time is often u…

  2. arXiv cs.CV TIER_1 English(EN) · Ruisheng Su ·

    StrokeTimer: Robust Representation Learning for Ischemic Stroke Onset-Time Estimation from Non-contrast CT

    Ischemic stroke is a major global disease. Treatment decisions are highly time-sensitive, as eligibility for reperfusion therapies relies on the interval between stroke onset and intervention. However, the true onset time is often uncertain in clinical practice, necessitating ima…

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