AI model trained on routine ECGs detects hidden patterns linked to sudden cardiac death risk, UC Berkeley study finds
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FOX News
Summary
UC Berkeley researchers have developed an AI model trained on routine ECGs that can detect hidden patterns associated with sudden cardiac death risk. The model identifies warning signs in standard heart test data that doctors have traditionally missed, potentially helping identify at-risk individuals — including younger athletes and people without known heart problems — before a cardiac arrest occurs. Sudden cardiac arrest kills hundreds of thousands of Americans annually, and survival rates outside hospitals are extremely low, making early detection critical.
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bskyAI model trained on routine ECGs detects hidden patterns linked to sudden cardiac death risk, UC Berkeley study findsfox7austin.comKey quotes
· 3 pulledA routine heart test may be hiding a warning sign that doctors have missed for years.
Sudden cardiac arrest can strike people with known heart problems. However, it can also hit younger athletes and people who never knew they were at risk.
Each year, hundreds of thousands of Americans die after cardiac arrest. Once it happens outside a hospital, survival ca
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