Most people know that heart attacks are dangerous, but fewer realize that sudden cardiac arrest is often even more deadly.
During sudden cardiac arrest, the heart’s electrical system suddenly stops working properly, causing the heart to beat in a chaotic way or stop beating altogether.
Blood can no longer reach the brain and other organs, and unless treatment begins immediately, survival is unlikely. Because the event happens without warning, preventing these deaths depends on finding high-risk people before symptoms appear.
Scientists at the University of California, Berkeley believe artificial intelligence may provide a better answer than current screening methods. Their new study, published in Nature, shows that AI can examine ordinary ECG heart recordings and detect warning signs that human experts may not easily recognize.
To create the system, researchers analyzed more than 440,000 ECG tests collected through Sweden’s national healthcare system. Each recording was matched with health records and death certificates, allowing the AI to learn which electrical patterns were linked with future sudden cardiac death.
The program was then evaluated using completely separate patient groups from hospitals in the United States and Taiwan to confirm that it worked in different populations.
The researchers found that the AI consistently predicted future sudden cardiac death more accurately than the commonly used measurement called ejection fraction, which estimates how well the heart pumps blood.
Many people who later died had never been considered high risk by traditional screening, suggesting that current methods miss a large number of vulnerable patients.
This discovery could have major practical value because implantable defibrillators can prevent many sudden cardiac deaths. These small devices are placed under the skin and automatically correct dangerous heart rhythms.
However, deciding who should receive one has always been difficult. Implanting the device involves surgery, expense, and possible complications, so doctors need reliable ways to identify patients who are most likely to benefit.
The investigators spent nearly a decade collecting and validating the enormous amount of information needed for this project. According to the research team, building reliable medical AI depends on access to large, carefully organized patient databases rather than computer technology alone.
The next stage of the research will involve using the AI within real healthcare systems. Patients identified as high risk may undergo additional heart monitoring before doctors decide on treatment.
The researchers also hope the hidden electrical patterns discovered by the AI will improve scientific understanding of why sudden cardiac arrest happens in the first place.
The findings are promising because they use a simple test that is already widely available in hospitals and clinics worldwide. Nevertheless, important questions remain. The study shows that AI predicts risk better than existing methods, but further clinical trials must demonstrate that acting on these predictions actually reduces deaths.
Researchers will also need to ensure that the system performs equally well across different populations and healthcare settings. Overall, this research represents an exciting advance in preventive heart care and demonstrates how artificial intelligence may help doctors identify invisible risks before a medical emergency occurs.
If you care about heart health, please read studies about how eating eggs can help reduce heart disease risk, and herbal supplements could harm your heart rhythm.
For more health information, please see recent studies about how drinking milk affects risks of heart disease and cancer, and results showing strawberries could help prevent Alzheimer’s disease.
Source: University of California, Berkeley.
