Millions of Americans have atrial fibrillation, but many of them don’t know it.
Atrial fibrillation (AFib) is notoriously hard to diagnose because its symptoms can be intermittent. Between two and six million Americans have AFib. AFib increases the risk of heart failure, blood clots, strokes, and other cardiac events.
Someone with atrial fibrillation may experience symptoms of the condition at irregular times. Typically, a patient with heart problems will undergo an electrocardiogram or EKG. An EKG is a recording of the heart’s electrical activity. A trained expert reviews the results of the test.
However, if the patient isn’t experiencing symptoms at the time of the test, AFib may not be detectable. To combat this, researchers at the Mayo Clinic recently released the results of a study where artificial intelligence was used to look at hundreds of thousands of EKG scans. Artificial intelligence found a way to potentially diagnose atrial fibrillation in as little as 10 seconds.
Applying artificial intelligence to EKG readings allows doctors to detect the signs of atrial fibrillation, even when larger symptoms aren’t occurring while the EKG is recorded.
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The Mayo Clinic research team developed artificial intelligence to scour almost 650,000 EKGs taken over 24 years from over 180,000 adult patients.
The AI was able to identify people with potentially undetected atrial fibrillation with 83 percent accuracy by finding signals in the EKG that might have otherwise been invisible to the human eye. Even well-trained experts may miss signs of AFib on an EKG.
The authors of the research say that one day, it may be possible to use AI as a diagnostic test to screen for hypertension, diabetes, or other high-risk diseases.
There are many potential applications for using AI to detect AFib, including predicting the types of people it may affect and helping detect it earlier.
If the research can be replicated, AI has the potential to help people with undiagnosed AFib who are at a high risk of stroke.
Eighty percent of strokes are preventable. Suppose artificial intelligence can identify patients who would have normally never been diagnosed until they have a stroke. In that case, huge impacts can be made on patients’ quality of life, disability, and longevity.
This research may prove that AI can help accurately detect serious irregular heartbeat issues, even when there’s no obvious evidence to the human eye. Patients could see this technology being used routinely in a few years once more robust data has been collected and a higher percentage of accuracy can be achieved in the larger population.
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AI might be scaled so it could be used in consumer products to help detect health problems. It could become very easy and seamless for people to get detected. This could enable long-term and more frequent EKGs, improving machine learning and improving EKG accuracy.
As the trend continues for more patient-directed care, AI-enabled technology could be used as a screening tool used by clinicians and as a direct consumer product, such as a function in a smartwatch.
However, until AI can detect AFib by using your smartwatch or smartphone, the Mayo Clinic’s research still must undergo more testing and scrutiny before it’s widely used.
This information can likely be best applied to patients with unexplained strokes. Many patients are diagnosed with unexplained strokes where undiagnosed atrial fibrillation is suspected. AI could significantly reduce the testing needed to detect atrial fibrillation.
This could improve and save people’s lives.
If you have concerns about strokes or cardiovascular disease, get peace of mind or early detection by being screened for atrial fibrillation.
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