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Integrating AI into diagnostics for heart transplant rejection

There are over 3,000 candidates on the heart transplant waiting list in the United States as of March this year. These numbers are available on the Organ Procurement and Transplantation Network’s (OPTN) online database.

According to OPTN, every 10 minutes another person is added to the national organ transplant waiting list–and that might include adult and pediatric heart transplant candidates. The heart transplant surgery has been dubbed as ‘a life-saving procedure’ for patients with end-stage heart disease or when other treatments are not effective. In the heart transplant surgery, a diseased heart is removed and replaced with a healthy heart from a brain-dead donor.

We can understand that the whole process is not without risks. Among many of these risks some are infection, failure of the donor heart, kidney failure and heart transplant rejection by a recipient when the body’s immune system attacks the transplanted organ.

Detecting heart transplant rejection is really challenging as patients may not experience symptoms in early stages of organ transplantation. The good news is: scientists have now started harnessing the power of artificial intelligence (AI) for health risk minimization in case of heart transplant.

A team of Harvard Medical School investigators at Brigham and Women’s Hospital have tried to address this very challenge by leveraging new advances in artificial intelligence (AI). They have created a deep learning-based AI system which promises identifying signs of heart transplant rejection. The AI-powered system called Cardiac Rejection Assessment Neural Estimator or CRANE can help detect not just heart transplant rejection but it can also estimate severity of rejection.

“Our results set the stage for large-scale clinical trials to establish the utility of AI models for improving heart transplant outcomes,” said Faisal Mahmood, HMS assistant professor of pathology at Brigham and Women’s, who is the senior author of the latest study.

In order to train CRANE for detecting transplant rejection, the team of HMS investigators used thousands of pathology images from over 1,300 heart biopsies from Brigham and Women’s. Then they validated the model, using test biopsies and independent, external test sets received from hospitals in Switzerland and Turkey.

Scientists believe that these efforts have paved the way for clinical trials to establish the efficacy of the system fueled by AI to improve heart transplant outcomes.

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