Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.

ABSTRACT: Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often leads to inappropriate treatment with immunosuppressive drugs, unnecessary follow-up biopsies and poor transplant outcomes. Here we present a deep learning-based artificial intelligence (AI) system for automated assessment of gigapixel whole-slide images obtained from EMBs, which simultaneously addresses detection, subtyping and grading of allograft rejection. To assess model performance, we curated a large dataset from the United States, as well as independent test cohorts from Turkey and Switzerland, which includes large-scale variability across pop

SUBMITTER: Lipkova J 

PROVIDER: S-EPMC9353336 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature