ArticleJournal of medical artificial intelligence2025
Qualitative American Heart Association plot of late gadolinium enhancement with mortality and ventricular arrhythmia prediction using artificial intelligence.
Article in Journal of medical artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
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Authors and funding
8 authors.
Funding
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).
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