ArticleEuropean heart journal. Digital health2025
Multi-modal artificial intelligence algorithm for the prediction of left atrial low-voltage areas in atrial fibrillation patient based on sinus rhythm electrocardiogram and clinical characteristics: a retrospective, multicentre study.
Article in European heart journal. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- ECGomics: An Open Platform for AI-ECG Digital Biomarker Discovery.Health data science · 2026Article
- Non-Invasive Prediction of Atrial Fibrosis Using a Regression Tree Model of Mean Left Atrial Voltage.Biomedicines · 2025Article
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Authors and funding
8 authors.
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Abstract
Aims: We aimed to develop an artificial intelligence (AI) algorithm capable of accurately predicting the presence of left atrial low-voltage areas (LVAs) based on sinus rhythm electrocardiograms (ECGs) in patients with atrial fibrillation (AF). Methods and results: The study included 1133 patients with AF who underwent catheter ablation procedures, with a total of 1787 12-lead ECG images analysed. Artificial intelligence-based algorithms were used to construct models for predicting the presence of LVAs. The DR-FLASH and APPLE clinical scores for LVAs prediction were calculated. A receiver operating characteristic (ROC) curve and a calibration curve were used to evaluate model performance. Multicentre validation included 92 AF patients from five centres, with a total of 174 ECGs. The data obtained from the participants were split into training ( Conclusion: The multimodal AI algorithm, which integrated ECG images and clinical features, predicted the presence of LVAs with a higher degree of accuracy than ECG alone and the clinical LVA scores.
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