Author name: kriston

Szferle et al. have developed an AI-based pathology workflow to objectively quantify and predict heart transplant rejection from biopsy images . This study, published in the Hungarian medical journal Orvosi Hetilap, centered on optimizing and applying our Biological Image Analysis Software (BIAS) to automatically identify cells and measure key morphological parameters that indicate rejection . The research, involving team members from our company, Single-Cell Technologies, successfully demonstrated that BIAS can quantify parameters like lymphocyte density and proximity to heart muscle cells, which strongly correlate with the severity of graft rejection, offering a new and powerful quantitative tool for pathologists .