AI may help catch heart transplant rejection without biopsies
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.
A new artificial intelligence study suggests that analyzing heart rhythm recordings and blood tests could accurately flag when a transplant patient's body starts attacking a donated heart, potentially eliminating the need for invasive biopsies. Researchers at NYU Langone Health trained AI models on EKG readings and blood tests to recognize patterns that indicate transplant rejection, a common but treatable complication of heart transplants.
The combined AI model, tested on 38 additional patients, correctly identified 94% of those without rejection, compared to 81% accuracy with blood tests alone. By reducing false positives, the combined model could spare patients from unnecessary biopsies and prompt earlier treatment. The study, published in JHLT Open, is the first to combine blood biomarkers with EKG data in an AI model, with researchers planning to test the model in more patients at various transplant centers.
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