The system, which discovered hidden signals in routine medical diagnostic tests, could help doctors better prevent strokes and other cardiovascular issues in people with atrial fibrillation, the most prevalent type of heart rhythm disorder
Published Date – 10:59 PM, Wed – 18 October 23
The system, which discovered hidden signals in routine medical diagnostic tests, could help doctors better prevent strokes and other cardiovascular issues in people with atrial fibrillation, the most prevalent type of heart rhythm disorder
Washington: Researchers discovered that an artificial intelligence (AI) system can detect an irregular cardiac rhythm in persons who do not yet have symptoms.
The system, which discovered hidden signals in routine medical diagnostic tests, could help doctors better prevent strokes and other cardiovascular issues in people with atrial fibrillation, the most prevalent type of heart rhythm disorder.
Previously developed algorithms were mostly employed on white people. This algorithm works in a variety of situations and patient demographics, including veterans and underserved people in the United States. The findings were reported in JAMA Cardiology, a peer-reviewed journal.
“This research allows for better identification of a hidden heart condition and informs the best way to develop algorithms that are equitable and generalizable to all patients,” said David Ouyang, MD, a cardiologist in the Department of Cardiology in the Smidt Heart Institute at Cedars-Sinai, a researcher in the Division of Artificial Intelligence in Medicine, and senior author of the study.
Experts estimate that about 1 in 3 people with atrial fibrillation do not know they have the condition.
In atrial fibrillation, the electrical signals in the heart that regulate the pumping of blood from the upper chambers to the lower chambers are chaotic. This can cause blood in the upper chambers to pool and form blood clots that can travel to the brain and trigger an ischemic stroke.
To create the algorithm, investigators programmed an artificial intelligence tool to study patterns found in electrocardiogram readings. An electrocardiogram is a test that monitors electrical signals from the heart. People who undergo this test have electrodes placed on their body that detect the heart’s electrical activity.
The algorithm was trained on almost a million electrocardiograms and it accurately predicted patients would have atrial fibrillation within 31 days.
The AI model was also applied to medical records from patients at Cedars-Sinai and it similarly–and accurately–predicted cases of atrial fibrillation within 31 days.
“This study of veterans was geographically and ethnically diverse, indicating that the application of this algorithm could benefit the general population in the U.S.,” said Sumeet Chugh, MD, director of the Division of Artificial Intelligence in Medicine in the Department of Medicine and medical director of the Heart Rhythm Center in the Department of Cardiology.
“This research exemplifies one of the many ways that investigators in the Smidt Heart Institute and the Division of Artificial Intelligence in Medicine are using AI to address preemptive management of complex and challenging cardiac conditions.”
