Ensemble learning of myocardial displacements for myocardial infarction detection in echocardiography Article Swipe
Tuan Hoang Nguyen
,
Phi Duong Nguyễn
,
Dai Q. Tran
,
Hung N. Pham
,
Quang Nguyen
,
Thanh Le
,
Hanh Van
,
B. Do
,
Phuong T. Tran
,
Lê Sỹ Vinh
,
Nguyễn Thị Thanh Thủy
,
Long Quoc Tran
,
Hieu H. Pham
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fcvm.2023.1185172
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3389/fcvm.2023.1185172
Background Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. Existing attempts typically formulate this task as classification and rely on a single segmentation model to estimate myocardial segment displacements. However, there has been no examination of how segmentation accuracy affects MI classification performance or the potential benefits of using ensemble learning approaches. Our study investigates this relationship and introduces a robust method that combines features from multiple segmentation models to improve MI classification performance by leveraging ensemble learning. Materials and Methods Our method combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI. We validated the proposed approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for training and validation, and an E-Hospital dataset (60 echocardiograms) from a local clinical site in Vietnam for independent testing. Model performance was evaluated based on accuracy, sensitivity, and specificity. Results The proposed approach demonstrated excellent performance in detecting MI. It achieved an F1 score of 0.942, corresponding to an accuracy of 91.4%, a sensitivity of 94.1%, and a specificity of 88.3%. The results showed that the proposed approach outperformed the state-of-the-art feature-based method, which had a precision of 85.2%, a specificity of 70.1%, a sensitivity of 85.9%, an accuracy of 85.5%, and an accuracy of 80.2% on the HMC-QU dataset. On the external validation set, the proposed model still performed well, with an F1 score of 0.8, an accuracy of 76.7%, a sensitivity of 77.8%, and a specificity of 75.0%. Conclusions Our study demonstrated the ability to accurately predict MI in echocardiograms by combining information from several segmentation models. Further research is necessary to determine its potential use in clinical settings as a tool to assist cardiologists and technicians with objective assessments and reduce dependence on operator subjectivity. Our research codes are available on GitHub at https://github.com/vinuni-vishc/mi-detection-echo .
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fcvm.2023.1185172
- https://www.frontiersin.org/articles/10.3389/fcvm.2023.1185172/pdf?isPublishedV2=False
- OA Status
- gold
- Cited By
- 6
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387609764
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387609764Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fcvm.2023.1185172Digital Object Identifier
- Title
-
Ensemble learning of myocardial displacements for myocardial infarction detection in echocardiographyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-13Full publication date if available
- Authors
-
Tuan Hoang Nguyen, Phi Duong Nguyễn, Dai Q. Tran, Hung N. Pham, Quang Nguyen, Thanh Le, Hanh Van, B. Do, Phuong T. Tran, Lê Sỹ Vinh, Nguyễn Thị Thanh Thủy, Long Quoc Tran, Hieu H. PhamList of authors in order
- Landing page
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https://doi.org/10.3389/fcvm.2023.1185172Publisher landing page
- PDF URL
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https://www.frontiersin.org/articles/10.3389/fcvm.2023.1185172/pdf?isPublishedV2=FalseDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fcvm.2023.1185172/pdf?isPublishedV2=FalseDirect OA link when available
- Concepts
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Segmentation, Artificial intelligence, Classifier (UML), Myocardial infarction, Computer science, Pattern recognition (psychology), Ensemble learning, Sensitivity (control systems), Machine learning, Medicine, Cardiology, Engineering, Electronic engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 4, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.displacements. | 54 |
| abstract_inverted_index.interventions. | 19 |
| abstract_inverted_index.echocardiograms | 288 |
| abstract_inverted_index.echocardiograms) | 145, 155 |
| abstract_inverted_index.state-of-the-art | 217 |
| abstract_inverted_index.echocardiographic | 33 |
| abstract_inverted_index.https://github.com/vinuni-vishc/mi-detection-echo | 333 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5065112274, https://openalex.org/A5078144870 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I4210142044, https://openalex.org/I67868205 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.85324723 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |