AI System Helps Spot Signs of Heart Transplant Rejection
An artificial intelligence (AI) tool can help identify heart transplant rejection and estimate its severity, results of a pilot study suggest. The Cardiac Rejection Assessment Neural Estimator (CRANE) simultaneously addresses detection, subtyping, and grading of allograft rejection in H&E-stained whole-slide images of endomyocardial biopsy samples and is intended to be used in conjunction with the heart transplant team to more quickly and accurately diagnose rejection.
@gilli Endomyocardial biopsy screening is the standard of care for detecting cardiac allograft rejection, but manual interpretation of surveillance endomyocardial biopsies remains a challenge, the authors note. Experts often disagree on whether or not the patient is rejecting the allograft and on the degree of severity of rejection when present. Overestimation of rejection can lead to patient anxiety, overtreatment and unnecessary follow-up biopsies, whereas underestimation can lead to delays in treatment and worse outcomes.