| Title: | Image based artificial intelligence-enhanced electrocardiogram prediction of incident atrial fibrillation |
| Journal: | Heart Rhythm |
| Published: | 14 Oct 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41101371/ |
| DOI: | https://doi.org/10.1016/j.hrthm.2025.10.024 |
Publication 19179
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
BACKGROUND: Early prediction of atrial fibrillation (AF) is crucial for reducing adverse outcomes. While artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise in predicting AF, most approaches require digital ECG signals, limiting their application in settings where ECGs are stored as images.</p>
OBJECTIVE: We aimed to develop and validate an image-based AI-ECG approach for predicting incident AF across multiple datasets.</p>
METHODS: We used 1,163,401 ECGs from 189,539 patients in the Beth Israel Deaconess Medical Center (BIDMC) dataset and 70,655 ECGs from 65,610 participants in the United Kingdom (UK) Biobank. The AI-ECG model was trained on ECG images processed to 310x868 pixels.</p>
RESULTS: The model achieved C-statistics of 0.754 (95% confidence interval [CI]: 0.747-0.761) in the BIDMC dataset and 0.723 (95% CI: 0.704-0.741) in the UK Biobank for predicting incident AF. Performance was maintained across key subgroups including outpatients, women, and non-white individuals. Compared with the CHARGE-AF risk score, the AI-ECG model showed superior performance (c-statistic 0.696 vs 0.667, P < .05) and provided significant additive value when combined (c-statistic 0.711, P < .0001). The model also performed well on smartphone-photographed ECGs (c-statistic 0.736). Saliency mapping indicated the model primarily focused on P-wave morphology and PR interval regions.</p>
CONCLUSION: This image-based approach enables AI-ECG prediction of AF in settings without digital ECG infrastructure and provides additive value to known clinical risk scores.</p>
12 Keywords
- Aged
- Artificial Intelligence
- Atrial Fibrillation
- Electrocardiography
- Female
- Humans
- Incidence
- Male
- Middle Aged
- Predictive Value of Tests
- Risk Assessment
- United Kingdom
12 Authors
- Boroumand Zeidaabadi
- Konstantinos Patlatzoglou
- Joseph Barker
- Libor Pastika
- Gul Rukh Khattak
- Mehak Gurnani
- Xavier Da Silva Anjos Machado
- Nicholas S Peters
- Daniel B Kramer
- Jonathan W Waks
- Fu Siong Ng
- Arunashis Sau
1 Application
| Application ID | Title |
|---|---|
| 48666 | Electrophysiological remodelling in patients at elevated risk of ventricular arrhythmia and sudden cardiac death |
Enabling scientific discoveries that improve human health