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2024 preprint

Detecting Incipient Heart Failure in Asymptomatic Patients with Normal Ejection Fraction and comparisons with patients with heart failure and preserved ejection fraction using TimeSformer for classifying Echocardiography videos

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4Institutions déclarées
3Pays d’affiliation déclarés

Rattachement africain : in, cn, us. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

Abstract 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.

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Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Detecting Incipient Heart Failure in Asymptomatic Patients with Normal Ejection Fraction and comparisons with patients with heart failure and preserved ejection fraction using TimeSformer for classifying Echocardiography videos
Date Crossref
29/10/2024
Éditeur
openRxiv
Type
posted-content

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Les institutions déclarées

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Artificial Intelligence in HealthcareCardiovascular Function and Risk FactorsIntravenous Infusion Technology and Safety

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