Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning.

ABSTRACT:

Aims

One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system.

Methods and results

We collected 1079 histopathology slides from 325 patients from three transplant centres in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross-validation and by deploying it to three cohorts.

SUBMITTER: Seraphin TP 

PROVIDER: S-EPMC10232288 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature