An artificial intelligence-enabled electrocardiogram algorithm for the prediction of left atrial low-voltage areas in persistent atrial fibrillation

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

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 (n = 906), validation (n = 113), and test sets (n = 114). Low-voltage areas were detected in 47.4% of all participants. Using ECG alone, the convolutional neural network (CNN) model achieved an area under the ROC curve (AUROC) of 0.704, outperforming both the DR-FLASH score (AUROC = 0.601) and the APPLE score (AUROC = 0.589). Two multimodal AI models, which integrated ECG images and clinical features, demonstrated higher diagnostic accuracy (AUROC 0.816 and 0.796 for the CNN-Multimodal and CNN-Random Forest-Multimodal models, respectively). Our models also performed well in the multicentre validation dataset (AUROC 0.711, 0.785, and 0.879 for the ECG alone, CNN-Multimodal, and CNN-Random Forest-Multimodal models, respectively).

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.