Deep learning analysis of sinus-rhythm echocardiography for the identification of patients with atrial fibrillation

Paroxysmal atrial fibrillation (AF) is underdiagnosed due to its intermittent and asymptomatic nature. We evaluated whether a deep neural network (DNN) applied to transthoracic echocardiograms (TTEs) acquired in sinus rhythm can discriminate between patients with and without documented AF and identify individuals at increased risk for subsequent AF documentation and adverse outcomes.

We retrospectively analysed 85 686 TTE studies (2009–21). A DNN was applied to apical four-chamber and parasternal long-axis videos without Doppler. AF ground truth was based on International Classification of Diseases (ICD) codes, electrocardiogram, Holter, or prior echocardiographic documentation of AF. The dataset was split into training (60%), validation (20%), and an independent test set (20%). In the test cohort, we assessed discrimination performance. Among patients without prior AF, we evaluated associations between model classification and subsequent AF documentation and a composite outcome of all-cause mortality, heart failure hospitalization, or stroke. The test set included 17 121 patients (median age 66 years [interquartile range, IQR 54–75]; 57.6% male), of whom 3449 (20.1%) had prior AF. The model achieved an area under the receiver operating characteristic curve of 0.85, with sensitivity 0.80 and specificity 0.72. At the selected threshold, 6581 patients (38.4%) were classified as model-positive, including 3832 (58.2%) without prior AF. Among patients without prior AF, model-positive classification was associated with subsequent AF documentation [adjusted hazard ratio (HR) 1.64; 95% confidence interval (CI) 1.33–2.04] and with the composite outcome (adjusted HR 1.24; 95% CI 1.13–1.35).

Deep learning applied to sinus-rhythm TTE demonstrates discrimination for AF and identifies an imaging-derived signal associated with subsequent AF documentation and adverse outcomes.