Purpose of review: Cardiac imaging and sequencing have greatly improved over the recent years. The goal of this review is to summarize these recent advances in cardiac imaging and sequencing, their application in heart-transplantation, and provide our perspective in how artificial intelligence provides a new paradigm for big data driven analysis in heart-transplant research. Recent findings: Cardiac imaging, particularly parametric mapping by cardiac MRI and global longitudinal strain by echocardiography, has improved our understanding of cardiac allograft rejection and prediction of adverse clinical outcomes. Independently, gene expression profiling and measurement of donor-derived cell free DNA have greatly improved risk stratification for acute rejection. More recently, data-driven phenotypic clustering using novel machine learning algorithms has been used to identify a distinct macrophage subset, associated with acute rejection. Summary: Developments in imaging and sequencing techniques in the application of heart-transplant research are improving rapidly and in parallel with improvements in analysis of these large datasets. The approach to heart-transplant research is in the transition of significant change as big data driven analysis identifies new mechanistic patterns that can be combined with traditional hypothesis testing.
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