AI Predicts Stroke 6 Hours Early with 99% Accuracy Using Wearable Data
This breakthrough could save thousands of lives by turning smartwatches into early warning systems.
Deep Dive
Researchers have developed an AI model that can predict an in-hospital stroke up to 6 hours before it occurs by analyzing heart rate data (PPG) from standard monitors. The system, tested on real-world clinical data from over 300 patients, achieved up to 99% accuracy. It uses an LLM to mine medical notes for precise stroke timestamps and a ResNet-1D model to analyze pre-stroke physiological patterns, providing the first empirical evidence that passive monitoring can enable reliable early warnings.
Why It Matters
This shifts stroke care from reactive treatment to proactive, life-saving prevention using existing hospital equipment.