By analyzing heart rhythm recordings and blood tests, artificial intelligence may accurately flag when a transplant patient's body starts attacking a donated heart, a new study suggests.
Led by NYU Langone Health researchers, the study explored an alternative method: whether combining electrocardiograms (EKGs)-which record the heart's electrical activity using sensors placed on the skin-with blood tests could help identify rejection without the need for an invasive biopsy in many cases.
The team trained AI models to recognize patterns in 5,300 EKG readings taken in 2,357 adult heart transplant recipients.
In a test group of an additional 38 male and female heart transplant recipients, the researchers found that the combined model performed better, correctly identifying 94 percent of patients who were not experiencing rejection.
By contrast, the model based on blood tests incorrectly flagged 19 patients as potentially needing a biopsy.
The combined model, the researchers said, would have spared the patients from the procedure.
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