Qualitative American Heart Association plot of late gadolinium enhancement with mortality and ventricular arrhythmia prediction using artificial intelligence.

ABSTRACT:

Background

The prognostic value of late gadolinium enhancement (LGE) in cardiac magnetic resonance (CMR) imaging is well-established. However, the direct relationship between image pixels and outcomes remains poorly understood. We hypothesised that leveraging artificial intelligence (AI) to analyse qualitative LGE images based on American Heart Association (AHA) guidelines could elucidate this relationship.

Methods

We collected retrospective CMR cases from a stress perfusion database, selecting LGE images comprising three long-axis views and 10 short-axis views. Clinical CMR reports served for annotation. We trained a multi-label convolutional neural network (CNN) to predict each AHA segment. Additionally, we transformed LGE image pixels into features, combined them with cli

SUBMITTER: Alskaf E 

PROVIDER: S-EPMC7617223 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature