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 clinical data features, and trained a hybrid neural network (HNN) to predict mortality and ventricular arrhythmia. The dataset was divided into training (70%), validation (15%), and test (15%) sets. Evaluation metrics included the area under the curve (AUC). RESULTS: The total number of cases included was 2,740, with 218 patients experiencing positive mortality events (8%). The total number of cases with at least one AHA segment positive for LGE was 823 (30%), among which 111 (13%) experienced mortality events, and 84 (10%) had ventricular arrhythmia events. When assessing all segments combined, the most common cases were those classified as normal studies, with each AHA segment having a score of 0 (1,661 cases, 60.6%). The multi-label classifier demonstrated fair performance (AUC: 64%), whereas the cluster classifier did not yield any predictions (AUC: 53%, P<0.001). The mortality HNN achieved a satisfactory performance with an AUC of 77%, as did the ventricular arrhythmia HNN with an AUC of 75%. CONCLUSIONS: Our study demonstrates the feasibility of generating qualitative AHA LGE maps using AI. Furthermore, the prediction of mortality and ventricular arrhythmia using HNN represents a potent new approach for risk stratification in patients with known or suspected coronary artery disease (CAD).