# Early Myocardial Infarction Detection over Multi-view Echocardiography

arXiv: https://arxiv.org/abs/2111.05790 · HTML: https://mlsanity.com/paper/2111.05790/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 67 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Deep Snake for Real-Time Instance Segmentation](https://mlsanity.com/paper/2001.01629/) (2020)
- [Early Detection of Myocardial Infarction in Low-Quality Echocardiography](https://mlsanity.com/paper/2010.02281/) (2020)
- [Left Ventricular Wall Motion Estimation by Active Polynomials for Acute Myocardial Infarction Detection](https://mlsanity.com/paper/2008.04615/) (2020)

## Built on by (most cited first, 9 of 9)

- [CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation](https://mlsanity.com/paper/2408.00181/) (2024)
- [Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography](https://mlsanity.com/paper/2204.07253/) (2022)
- [Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification](https://mlsanity.com/paper/2402.06530/) (2024)
- [Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins](https://mlsanity.com/paper/2606.01808/) (2026)
- [Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound Image Segmentation](https://mlsanity.com/paper/2502.03781/) (2025)
- [One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks](https://mlsanity.com/paper/2503.14979/) (2025)
- [An Edge-aware Prompt-enhanced SAM for Ultrasound Image Segmentation](https://mlsanity.com/paper/2607.07240/) (2026)
- [Motion-Conditioned Multi-View Fusion for Myocardial Infarction Localization from Echocardiography](https://mlsanity.com/paper/2607.15268/) (2026)
- [Domain Shift in Echocardiography: Interpretable Quantification and Prediction of Cross-Dataset Left Ventricular Segmentation](https://mlsanity.com/paper/2607.19643/) (2026)

## Abstract (arXiv)

Myocardial infarction (MI) is the leading cause of mortality in the world that occurs due to a blockage of the coronary arteries feeding the myocardium. An early diagnosis of MI and its localization can mitigate the extent of myocardial damage by facilitating early therapeutic interventions. Following the blockage of a coronary artery, the regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in. Echocardiography is the fundamental tool to assess any RWMA. Assessing the motion of the left ventricle (LV) wall only from a single echocardiography view may lead to missing the diagnosis of MI as the RWMA may not be visible on that specific view. Therefore, in this study, we propose to fuse apical 4-chamber (A4C) and apical 2-chamber (A2C) views in which a total of 12 myocardial segments can be analyzed for MI detection. The proposed method first estimates the motion of the LV wall by Active Polynomials (APs), which extract and track the endocardial boundary to compute myocardial segment displacements. The features are extracted from the A4C and A2C view displacements, which are concatenated and fed into the classifiers to detect MI. The main contributions of this study are 1) creation of a new benchmark dataset by including both A4C and A2C views in a total of 260 echocardiography recordings, which is publicly shared with the research community, 2) improving the performance of the prior work of threshold-based APs by a Machine Learning based approach, and 3) a pioneer MI detection approach via multi-view echocardiography by fusing the information of A4C and A2C views. Experimental results show that the proposed method achieves 90.91% sensitivity and 86.36% precision for MI detection over multi-view echocardiography. The software implementation is shared at https://github.com/degerliaysen/MultiEchoAI.