Deep Learning Resolves Myovascular Dynamics in the Failing Human Heart

The adult mammalian heart harbors minute levels of cycling cardiomyocytes (CMs). Large numbers of images are needed to accurately quantify cycling events using microscopy -based methods. CardioCount is a new deep learning-based pipeline to rigorously score nuclei in microscopic images. When applied to a repository of 368,434 human microscopic images, we found evidence of coupled growth between CMs and cardiac endothelial cells in the adult human heart. Additionally, we found that vascular rarefaction and CM hypertrophy are interrelated in end -stage heart failure. CardioCount is available for use via GitHub and via Google Colab for users with minimal machine learning experience. (J Am Coll Cardiol Basic Trans Science 2024;9:674-686) (c) 2024 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).