UC Berkeley AI Identifies Hidden EKG Signals Linked to Sudden Cardiac Death Risk
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- ScienceDaily.com
Researchers at UC Berkeley used artificial intelligence to identify previously overlooked electrical patterns in routine electrocardiograms (EKGs) that may help detect people at risk of sudden cardiac death. The system was trained on more than 440,000 EKGs collected in Sweden, matched with death-certificate information, and evaluated using thousands of additional patient records from the United States and Taiwan. The study was published in Nature.
The findings suggest the AI could distinguish high-risk patients more effectively than conventional screening based largely on how much blood the heart pumps with each contraction. Conventional measurements identified a group with a 4.6% annual sudden cardiac death rate; the AI identified a group with a 7% annual rate. It also flagged a broader population, including thousands of people who would generally be considered relatively low risk under existing guidelines. Because the system analyzes ordinary EKG images already collected in hospitals and medical centers, it could potentially support screening without a new type of test.
The researchers’ next unresolved question is what changes in the heart produce the electrical patterns recognized by the AI. The findings could eventually inform physicians’ decisions about implanted defibrillators, but the article does not disclose a clinical deployment timeline. Ziad Obermeyer, the study’s lead author and a UC Berkeley School of Public Health associate professor, said the work could help improve medical decisions and understanding of patients before cardiac arrest.
Original report
AI discovers a hidden heart signal that could predict sudden death
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