Predicting long-term time to cardiovascular incidents using myocardial perfusion imaging and deep convolutional neural networks.

ABSTRACT: Myocardial perfusion imaging (MPI) is a clinical tool which can assess the heart's perfusion status, thereby revealing impairments in patients' cardiac function. Within the MPI modality, the acquired three-dimensional signals are typically represented as a sequence of two-dimensional grayscale tomographic images. Here, we proposed an end-to-end survival training approach for processing gray-scale MPI tomograms to generate a risk score which reflects subsequent time to cardiovascular incidents, including cardiovascular death, non-fatal myocardial infarction, and non-fatal ischemic stroke (collectively known as Major Adverse Cardiovascular Events; MACE) as well as Congestive Heart Failure (CHF). We recruited a total of 1928 patients who had undergone MPI followed by coronary interventions. A

SUBMITTER: Li YL 

PROVIDER: S-EPMC10869727 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature