Deep Learning Prediction of New-Onset Atrial Fibrillation Using Echocardiography Videos

AHA Scientific Sessions 2022 PRESENTATION
Authors Alvaro E Ulloa Cerna, Sushravya Raghunath, David vanMaanen, Jeffrey Ruhl, Brandon Fornwalt, John Pfeifer and Christopher M Haggerty

Introduction: Patients at high risk of undiagnosed Atrial Fibrillation (AF) can be identified from 12-lead ECG and routed to increased monitoring via wearable devices such as a watch or an ECG patch. A similar prediction task may be feasible employing echocardiography, increasing the likelihood of detecting patients at high risk for AF. Hypothesis: We hypothesized that by leveraging a large echocardiography database, a model can be trained to accurately predict future AF onset in patients undergoing routine echocardiography.

Methods: We collected data on patients with an echo at Geisinger (769,854 studies from 337,388 patients) and randomly grouped patients into training (80%) or testing (20%) sets. In the training cohort, studies were considered positive if they were obtained from patients with prior or new (<1 year) AF diagnosis. Alternatively, negative studies were those obtained from AF-free patients (>1 year). For testing, we selected one random echo per patient and restricted the positives to patients without prior AF diagnosis or new AF within 7 days of the echo. This was to avoid predicting positive AF at the time of echo. We tested the model on 38,808 valid studies where 5% developed AF within a year. We trained six convolutional neural networks, one per view (apical two, four and five chambers, basal and mid short-axis, and parasternal long-axis), and combined their outputs to train an XGboost model.

Results: Among the six views, the apical four chamber yielded the best performance with an area under the ROC curve (AUROC) of 76%, followed by the apical two chamber with 65%, the apical five chamber with 58%, the parasternal long-axis with 57%, the basal short-axis with 56%, and the mid short-axis with 50%. A model combining all six planes yielded a 78% AUROC.

Conclusions: CNN models trained on echocardiography videos can predict new-onset AF with moderate performance. We also found that views that captured the left atrium had better performance compared to those that did not.