Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging.

ABSTRACT:

Background

Machine learning (ML) has shown promise in improving the risk prediction in non-invasive cardiovascular imaging, including SPECT MPI and coronary CT angiography. However, most algorithms used remain black boxes to clinicians in how they compute their predictions. Furthermore, objective consideration of the multitude of available clinical data, along with the visual and quantitative assessments from CCTA and SPECT, are critical for optimal patient risk stratification. We aim to provide an explainable ML approach to predict MACE using clinical, CCTA, and SPECT data.

Methods

Consecutive patients who underwent clinically indicated CCTA and SPECT myocardial imaging for suspected CAD were included and followed up for MACEs. A MACE was defined as a composite outcome that

SUBMITTER: Alahdab F 

PROVIDER: S-EPMC10651041 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature