Can AI-Based Detection Prevent Heart-Related Complications?

Researchers at the Smidt Heart Institute at Cedars-Sinai have discovered that an artificial intelligence (AI) algorithm can identify atrial fibrillation, an irregular heart rhythm condition in individuals who have not yet displayed any symptoms. (1^✔ ✔Trusted Source
Deep learning of electrocardiograms in sinus rhythm from US Veterans to predict atrial fibrillation

Go to source) Previously developed algorithms have been primarily used in white populations. This algorithm works in diverse settings and patient populations, including U.S. veterans and underserved populations. The findings were published today in the peer-reviewed journal JAMA Cardiology.

Pioneering Research for Identifying Hidden Heart Conditions and Advancing Equitable Algorithms

“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 program was trained to analyze electrocardiogram readings taken between Jan. 1, 1987, and Dec. 31, 2022, from patients seen at two Veterans Affairs health networks. 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.”

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The study was a collaborative effort between physicians and investigators at Cedar-Sinai and the San Francisco and Palo Alto Veterans Affairs hospitals. In addition to Ouyang, Cedars-Sinai investigators Grant Duffy and John Theurer worked on the study.

Reference:

1. Deep learning of electrocardiograms in sinus rhythm from US Veterans to predict atrial fibrillation - (https://pubmed.ncbi.nlm.nih.gov/37851434/)

Source-Eurekalert