A
system and method for distinguishing non-failing from failing cardiac fibroblasts (CFs) using advanced imaging techniques and
machine learning (ML). The method involves
staining CFs with fluorescent dyes to highlight key cellular components, such as nuclei and
actin fibers. Images are analyzed to extract morphological features, which are then processed and normalized. A supervised ML
system, trained on labeled CF data, classifies CFs based on phenotypic differences. The
system enables accurate diagnosis of
heart failure (HF), facilitates screening for therapeutic
efficacy, and identifies antifibrotic agents by evaluating changes in CF phenotypes pre- and post-treatment.