Predicting Transplant Rejection with AI

About 4,500 heart transplants are performed in the US yearly. While this lifesaving operation improves quality of life and longevity for most recipients, organ rejection remains a risk, with acute rejection occurring in up to 32 percent of recipients within the first year.

A team of researchers from Emory, Case Western Reserve, and the University of Pennsylvania developed artificial intelligence tools to examine cardiac biopsy images to improve the prediction of rejection.

Clinicians now rely on histologic grading of cardiac biopsies to diagnose rejection, but the current criteria is vague and lacks diagnostic accuracy.

These limitations subject patients to considerable risk of receiving excessive or inadequate treatment.

The research team created a new method for automated, comprehensive analysis of heart biopsy images, the Cardiac Allograft Rejection Evaluator (CARE).

Using AI tools, CARE extracts features associated with the shape, texture and spatial architecture of muscle cells, immune cells, and stromal fiber in heart tissue specimen images to predict rejection outcomes for heart transplant patients.

“This facilitates the use of more aggressive treatments for those in need, leading to more effective prevention of heart transplant failure,” says researcher Sara Arabyarmohammadi of Case Western.

A study of 2,900 patients recently published in Circulation: Heart Failure showed that the CARE model, optimized to predict cardiac rejection severity, was far better at assessing a patient’s clinical outcome.

“What is most interesting is not just that the AI approach was able to better predict transplant rejection compared to pathologic grade, but it used a set of image features that were far more intuitive and explainable,” says senior author Anant Madabhushi, Woodruff Professor of Biomedical Engineering and executive director of the Emory Empathetic AI for Health Institute (AI.Health) at Emory. 

 

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