 The use of artificial intelligence, AI, techniques has enabled researchers to identify and characterize populations and subpopulations of different cell types in hearts recovering from myocardial infarction, MI. AI auto-encoding separates data from different cell types and subpopulations of cell types, cluster analysis. AI sparse modeling identifies genes and signaling mechanisms that are differentially activated between subpopulations, pathway, gene set enrichment analysis, and AI semi-supervised learning tracks the transformation of cells from one subpopulation into another, trajectory analysis. Auto-encoding was often used in data denoising, however, in our pipeline, auto-encoding was exclusively used for cell embedding and clustering. The performance of our AI SCI Nasek toolkit and other highly cited non-AI tools was evaluated with three SCI Nasek datasets obtained from the gene expression on Nibus database. Auto-encoder was the only tool to identify differences between the cardiomyocytes subpopulations found in mice that underwent MI or sham MI surgery on postnatal day P1. Statistically significant differences between cardiomyocytes from this article was offered by Ton Nguyen, Yuhua Wei, Yuji Nakada, and others. We are article.tv links in the description below.