Deep Learning Evaluation of Echocardiograms to Identify Occult Atrial Fibrillation

Background Atrial fibrillation (AF) can often be missed by intermittent screening given its frequently paroxysmal and asymptomatic presentation. Deep learning algorithms have been developed to identify patients with paroxysmal AF from electrocardiograms (ECGs) in sinus rhythm. Transthoracic echocardiograms (TTEs) may provide additional structural information complementary to ECGs that could also be used to help identify occult AF. Objective We sought to determine whether deep learning evaluation of echocardiograms of patients in sinus rhythm could identify occult AF. Methods We identified patients who had TTEs performed between 2004 and 2021. We created a two-stage model that (1) distinguished which TTEs were in sinus rhythm and which were in AF and then (2) predicted which of the TTEs in sinus rhythm were in patients with paroxysmal AF. Models were trained from video-based convolutional neural networks using TTE parasternal long axis (PLAX) videos. The AF prediction performance was compared to prediction using clinical variables, CHADSVASc score, and left atrial (LA) size. Results Our model trained on 111,319 TTE videos distinguished TTEs in AF from those in sinus rhythm with high accuracy (AUC 0.96, 0.95-0.96). A total of 72,181 TTE videos were in sinus rhythm. When tested on a held-out sample, the model predicted the occurrence of concurrent AF with an AUC of 0.71 (0.69-0.73). Using the max F1 threshold, the PPV was 0.20 and the NPV was 0.95. The model performed better than predicting concurrent AF using clinical risk factors (AUC 0.67, 0.65-0.69), LA area (AUC 0.63, 0.62-0.64), and CHADSVASc (AUC 0.61, 0.60-0.62). Conclusion A deep learning model distinguished AF from sinus rhythm TTEs with high accuracy and predicted the presence of AF within 90 days of sinus rhythm TTEs moderately well, better than clinical variables or LA size alone. TTEs may help inform automated opportunistic AF screening efforts. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement No funding was provided for this work. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Not Applicable The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the IRB at Cedars-Sinai Medical Center. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Not Applicable I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Not Applicable I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Not Applicable All data produced in the present study are available upon reasonable request to the authors.