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Estimating Ejection Fraction from Echocardiogram Data: A Narrative Review on Deep-Learning-Based Methods
Roshanak Ghorbani , Hamid Behnam *
Abstract:   (13 Views)
In recent years, many studies have used deep learning (DL) methods to estimate ejection fraction (EF) from two-dimensional echocardiogram data. DL methods offer several advantages. They automate EF estimation, overcome the limitations of manual or semi-automatic methods, and improve estimation accuracy and computational speed. Despite significant advances in DL-based EF estimation methods, the number of review papers examining their pipelines and performances is limited. In the current study, we categorized the DL-based EF estimation methods. We also reviewed their pipelines, architectures, performances, advantages, and limitations. Moreover, we discussed the current status, research trends, challenges, and future direction of this field. Finally, we addressed several open challenges for future studies. The results revealed that the field is mature yet evolving. DL-based methods have shown great potential for estimating EF. Additionally, many recent studies have focused on the clinical integration and evaluation of these methods.

 
Keywords: Artificial Intelligence, Deep Learning, Echocardiography, Ejection Fraction, Machine Learning, Review.
Full-Text [PDF 1234 kb]   (5 Downloads)    
Type of Study: Review Paper | Subject: Biomedical Signal & Image Processing
Received: 2026/04/05 | Revised: 2026/11/08 | Accepted: 2026/10/27
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.