EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning
The ejection fraction (EF) is a vital metric for assessing cardiovascular function through cardiac ultrasound. Manual evaluation is time-consuming and exhibits high variability among observers. Deep-learning techniques offer precise and autonomous EF predictions, yet these methods often lack explain...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-01-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2506.pdf |
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| author | Waqas Ali Wesam Alsabban Muhammad Shahbaz Ali Al-Laith Bassam Almogadwy |
| author_facet | Waqas Ali Wesam Alsabban Muhammad Shahbaz Ali Al-Laith Bassam Almogadwy |
| author_sort | Waqas Ali |
| collection | DOAJ |
| description | The ejection fraction (EF) is a vital metric for assessing cardiovascular function through cardiac ultrasound. Manual evaluation is time-consuming and exhibits high variability among observers. Deep-learning techniques offer precise and autonomous EF predictions, yet these methods often lack explainability. Accurate heart failure prediction using cardiac ultrasound is challenging due to operator dependency and inconsistent video quality, resulting in significant interobserver variability. To address this, we developed a method integrating convolutional neural networks (CNN) and transformer models for direct EF estimation from ultrasound video scans. This article introduces a Residual Transformer Module (RTM) that extends a 3D ResNet-based network to analyze (2D + t) spatiotemporal cardiac ultrasound video scans. The proposed method, EFNet, utilizes cardiac ultrasound video images for end-to-end EF value prediction. Performance evaluation on the EchoNet-Dynamic dataset yielded a mean absolute error (MAE) of 3.7 and an R2 score of 0.82. Experimental results demonstrate that EFNet outperforms state-of-the-art techniques, providing accurate EF predictions. |
| format | Article |
| id | doaj-art-f5cf8b74a1ff4accaf25bd7990e91552 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-f5cf8b74a1ff4accaf25bd7990e915522025-01-23T15:05:06ZengPeerJ Inc.PeerJ Computer Science2376-59922025-01-0111e250610.7717/peerj-cs.2506EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learningWaqas Ali0Wesam Alsabban1Muhammad Shahbaz2Ali Al-Laith3Bassam Almogadwy4Computer Science Department, University of Engineering and Technology, Lahore, PakistanDepartment of Computer and Network Engineering, College of Computing, Umm Al-Qura University, Makkah, Saudi ArabiaComputer Science Department, University of Engineering and Technology, Lahore, PakistanComputer Science Department, University of Copenhagen, Copenhagen, DenmarkDepartment of Artificial Intelligence and Data Science, Taibah University, Medina, Saudi ArabiaThe ejection fraction (EF) is a vital metric for assessing cardiovascular function through cardiac ultrasound. Manual evaluation is time-consuming and exhibits high variability among observers. Deep-learning techniques offer precise and autonomous EF predictions, yet these methods often lack explainability. Accurate heart failure prediction using cardiac ultrasound is challenging due to operator dependency and inconsistent video quality, resulting in significant interobserver variability. To address this, we developed a method integrating convolutional neural networks (CNN) and transformer models for direct EF estimation from ultrasound video scans. This article introduces a Residual Transformer Module (RTM) that extends a 3D ResNet-based network to analyze (2D + t) spatiotemporal cardiac ultrasound video scans. The proposed method, EFNet, utilizes cardiac ultrasound video images for end-to-end EF value prediction. Performance evaluation on the EchoNet-Dynamic dataset yielded a mean absolute error (MAE) of 3.7 and an R2 score of 0.82. Experimental results demonstrate that EFNet outperforms state-of-the-art techniques, providing accurate EF predictions.https://peerj.com/articles/cs-2506.pdfMedical imagingEchocardiographyCNNTransformersHeart disease |
| spellingShingle | Waqas Ali Wesam Alsabban Muhammad Shahbaz Ali Al-Laith Bassam Almogadwy EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning PeerJ Computer Science Medical imaging Echocardiography CNN Transformers Heart disease |
| title | EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning |
| title_full | EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning |
| title_fullStr | EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning |
| title_full_unstemmed | EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning |
| title_short | EFNet: estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning |
| title_sort | efnet estimation of left ventricular ejection fraction from cardiac ultrasound videos using deep learning |
| topic | Medical imaging Echocardiography CNN Transformers Heart disease |
| url | https://peerj.com/articles/cs-2506.pdf |
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