Artificial Intelligence Improves Heart Transplant Outcomes

A new study sets the stage for large-scale clinical trials to establish the utility of AI models for improving heart transplant outcomes.

An artificial intelligence (AI) system known as the Cardiac Rejection Assessment Neural Estimator (CRANE) can help detect heart transplant rejection and estimate its severity.

Heart transplantation can be a lifesaving operation for patients with end-stage heart failure. However, many patients experience organ transplant rejection, in which the immune system begins attacking the transplanted organ.

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‘A new artificial intelligence tool performed well in detecting and assessing heart transplant rejection, compared to those from conventional assessments.’

To help address these challenges, investigators from Brigham and Women’s Hospital created a new artificial intelligence (AI) system CRANE.

In a pilot study, researchers evaluated CRANE’s performance on samples provided by patients from three different countries. The results are published in the journal Nature Medicine.

“Our retrospective pilot study demonstrated that combining artificial intelligence and human intelligence can improve expert agreement and reduce the time needed to evaluate biopsies,” said senior author Faisal Mahmood, Ph.D., from the Mahmood Lab at the Brigham’s Department of Pathology.

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Heart biopsies are commonly used to identify and grade the severity of organ rejection in patients after heart transplantation. However, several studies have shown that experts often disagree on whether the patient is rejecting the heart or on the degree of severity of the rejection.

The variability in diagnosis has direct clinical consequences, causing delays in treatment, unnecessary follow-up biopsies, anxiety, inadequate medication dosing, and, ultimately, worse outcomes.

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CRANE is designed to be used in tandem with an expert assessment to establish an accurate diagnosis faster, and it can also be used in settings where there may be few pathology experts available. When experts used the tool, it reduced disagreement between experts and decreased assessment time.

Researchers note that its use in clinical practice remains to be determined and plans to make further improvements to the system, but the results illustrate the potential of integrating AI into diagnostics.

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The time is right to make a shift by bringing together people with clinical expertise and those with expertise in computational science to develop assistive diagnostic tools.

Source-Medindia