AI Detects Hidden ECG Signal That Could Predict Sudden Cardiac Death Before Symptoms Appear
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Over 86% of individuals flagged by the AI would not have been identified through today's conventional methods, suggesting the technology may uncover hidden risk that clinicians currently miss.
A new AI model, developed by researchers at the University of California, Berkeley, has identified a previously unknown electrical pattern hidden in routine ECG recordings that could help predict sudden cardiac death, offering a promising tool to identify high-risk patients missed by conventional heart screening.
Researchers led by Associate Professor Ziad Obermeyer trained the system using more than 440,000 electrocardiograms (ECGs) collected in Sweden and matched them with national death certificate records.
The model was later validated using patient datasets from the United States and Taiwan.
Current screening largely depends on measuring Left Ventricular Ejection Fraction (LVEF), an indicator of how effectively the heart pumps blood.
Although reduced LVEF is considered a major risk factor, many patients who later experience sudden cardiac death have normal pumping function, limiting the effectiveness of existing risk assessment methods.
The researchers reported that the AI model identified a group of patients with an estimated annual sudden cardiac death risk of 7%, compared with 4.6% using current standard screening approaches.
More importantly, over 86% of individuals flagged by the AI would not have been identified through today's conventional methods, suggesting the technology may uncover hidden risk that clinicians currently miss.
"One thing that makes the problem very tragic, but also very well suited for AI, is that we have the cure for this problem," Obermeyer said. "If you knew you were one of the people who was going to drop dead, you would go to a cardiologist, and you'd get a defibrillator implanted. The problem is that doctors can't figure out who needs one before it's too late."
Researchers believe AI could improve patient selection by identifying those who may benefit from additional cardiac evaluation rather than replacing existing clinical judgement.
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