Digital pathology in cardiac transplant diagnostics: from biopsies to algorithms

In the field of heart transplantation, the ability to accurately and promptly diagnose cardiac allograft rejection is crucial. This comprehensive review explores the transformative role of digital pathology and computational pathology, especially through machine learning, in this critical domain. These methodologies harness large datasets to extract subtle patterns and valuable information that extend beyond hu-man perceptual capabilities, potentially enhancing diagnostic outcomes. Current research indicates that these computer-based systems could offer accuracy and performance matching, or even exceeding, that of expert pathologists, thereby introducing more objectivity and reducing observer variability. Despite promising results, several challenges such as limited sample sizes, diverse data sources, and the absence of standardized protocols pose significant barriers to the widespread adoption of these techniques. The future of digital pathology in heart transplantation diagnostics depends on utilizing larger, more diverse patient cohorts, standardizing data collection, processing, and evaluation protocols, and fostering collaborative research efforts. The integration of various data types, including clinical, demographic, and imaging information, could further refine diagnostic precision. As researchers address these challenges and promote collaborative efforts, digital pathology has the potential to become an integral part of clinical practice, ultimately improving patient care in heart transplantation.(c) 2023 Elsevier Inc. All rights reserved.

Automated summary

This comprehensive review explores the transformative role of digital pathology and computational pathology, especially through machine learning, in the field of heart transplantation. These methodologies have the potential to enhance diagnostic outcomes and reduce observer variability by extracting valuable information from large datasets beyond human perceptual capabilities. However, challenges such as limited sample sizes, diverse data sources, and the absence of standardized protocols hinder the widespread adoption of these techniques.