# EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks

arXiv: https://arxiv.org/abs/2208.14003 · HTML: https://mlsanity.com/paper/2208.14003/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Neural Relational Inference for Interacting Systems](https://mlsanity.com/paper/1802.04687/) (2018)
- [Ultrasound Video Transformers for Cardiac Ejection Fraction Estimation](https://mlsanity.com/paper/2107.00977/) (2021)

## Built on by (most cited first, 17 of 17)

- [Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis](https://mlsanity.com/paper/2303.12644/) (2023)
- [GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis](https://mlsanity.com/paper/2308.13217/) (2023)
- [SupReMix: Supervised Contrastive Learning for Medical Imaging Regression with Mixup](https://mlsanity.com/paper/2309.16633/) (2023)
- [EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms](https://mlsanity.com/paper/2307.12229/) (2023)
- [EchoNarrator: Generating natural text explanations for ejection fraction predictions](https://mlsanity.com/paper/2410.23744/) (2024)
- [ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography](https://mlsanity.com/paper/2512.03339/) (2025)
- [EchoDFKD: Data-Free Knowledge Distillation for Cardiac Ultrasound Segmentation using Synthetic Data](https://mlsanity.com/paper/2409.07566/) (2024)
- [ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion](https://mlsanity.com/paper/2508.17631/) (2025)
- [Generative Regression for Left Ventricular Ejection Fraction Estimation from Echocardiography Video](https://mlsanity.com/paper/2602.08202/) (2026)
- [ConFormer: A Novel Collection of Deep Learning Models to Assist Cardiologists in the Assessment of Cardiac Function](https://mlsanity.com/paper/2312.08567/) (2023)
- [Echo-E3Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation](https://mlsanity.com/paper/2503.17543/) (2025)
- [Mamba-3D as Masked Autoencoders for Accurate and Data-Efficient Analysis of Medical Ultrasound Videos](https://mlsanity.com/paper/2503.20258/) (2025)
- [ARM-Explainer -- Explaining and improving graph neural network predictions for the maximum clique problem using node features and association rule mining](https://mlsanity.com/paper/2511.22866/) (2025)
- [InfoMotion: A Graph-Based Approach to Video Dataset Distillation for Echocardiography](https://mlsanity.com/paper/2512.09422/) (2025)
- [Optimizing Point-of-Care Ultrasound Video Acquisition for Probabilistic Multi-Task Heart Failure Detection](https://mlsanity.com/paper/2602.13658/) (2026)
- [A Unified DINOv2-Based Framework for LVEF Estimation, GLS Dysfunction Classification, and Early Cardiotoxicity Prediction](https://mlsanity.com/paper/2608.14750/) (2026)
- [A Quantitative Evaluation Framework for Temporal Explainability in Echocardiographic Video Segmentation](https://mlsanity.com/paper/2609.08043/) (2026)

## Abstract (arXiv)

Ejection fraction (EF) is a key indicator of cardiac function, allowing identification of patients prone to heart dysfunctions such as heart failure. EF is estimated from cardiac ultrasound videos known as echocardiograms (echo) by manually tracing the left ventricle and estimating its volume on certain frames. These estimations exhibit high inter-observer variability due to the manual process and varying video quality. Such sources of inaccuracy and the need for rapid assessment necessitate reliable and explainable machine learning techniques. In this work, we introduce EchoGNN, a model based on graph neural networks (GNNs) to estimate EF from echo videos. Our model first infers a latent echo-graph from the frames of one or multiple echo cine series. It then estimates weights over nodes and edges of this graph, indicating the importance of individual frames that aid EF estimation. A GNN regressor uses this weighted graph to predict EF. We show, qualitatively and quantitatively, that the learned graph weights provide explainability through identification of critical frames for EF estimation, which can be used to determine when human intervention is required. On EchoNet-Dynamic public EF dataset, EchoGNN achieves EF prediction performance that is on par with state of the art and provides explainability, which is crucial given the high inter-observer variability inherent in this task.