Background: Recently deep learning models have helped differentiate echocardiography images of patients with heart failure with preserved ejection fraction (HFpEF) from normal controls. Our aim was to develop a model capable of detecting early signs of heart failure in asymptomatic patients with a normal ejection fraction. Methods: We employed the TimeSformer, a video transformer model that classifies video data using a novel attentionbased mechanism. This self-attention mechanism that diverges from traditional convolutional neural networks (CNNs). It focuses on relevant parts of the video across both space and time, split into spatial attention, which processes each frame individually, and temporal attention, which integrates information across different frames. The training and validation of the TimeSformer model were conducted on the same dataset of echocardiography videos from 50 normal controls and 80 patients diagnosed with HFpEF, employing 5-fold cross-validation to ensure robust performance evaluation. Results: The TimeSformer model effectively identified HFpEF in patients, as all diagnosed with HFpEF were flagged as abnormal. The echocardiography assessment, along with NT pro BNP levels, supported the diagnoses, with patients showing NT pro BNP levels of 1016±32 pg/ml. Conversely, 26 out of 50 normal controls were correctly identified. While 24 normal controls were identified as abnormal. Their NT pro BNP levels were 52±12 pg/ml. At 2 years 8( 33.3%) controls identified as abnormal developed symptoms of heart failure with NT pro BNP levels of 800±41 pg/ml. Conclusions: The TimeSformer model demonstrated capability in identifying subtle deviations indicative of incipient heart failure in videos of normal controls, despite normal NT pro BNP levels. A significant number of these controls developed heart failure with elevated NT pro BNP levels at 2 years.

The authors have declared no competing interest.

NOT APPLICABLE

NO FUNDING

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

St. John's Medical College Ethics Committee. IERB: 289/2023

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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.

Yes

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).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

DATA WILL BE AVAILABLE ON REQUEST TO EITHER AUTHOR