Incremental Value of Radiomics Features of Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection
This study proposed a novel three-stage EAT extraction method. We demonstrated that adding EAT radiomics features to a COVID-19 severity detection model results in increased accuracy and reduced uncertainty. The value of these features was also confirmed through feature importance ranking and visualization.
Authors
- Cláudio Tinoco Mesquita (ORCID: https://orcid.org/0000-0002-1466-9413)
- Weihua Zhou (ORCID: https://orcid.org/0000-0002-6039-959X)
- Daniel Gama das Neves (ORCID: https://orcid.org/0000-0002-3030-2718)
- Alair Augusto Sarmet Moreira Damas dos Santos (ORCID: https://orcid.org/0000-0002-8640-3657)
- Chuang Han (ORCID: https://orcid.org/0000-0002-1295-8991)
- Neng Dai (ORCID: https://orcid.org/0000-0003-3620-0699)
- Zhao Chen (ORCID: https://orcid.org/0000-0002-5782-3329)
- Yanting Li (ORCID: https://orcid.org/0000-0002-2528-747X)
- Fubao Zhu
- Yanhui Tian
- Ni Yao
- Wolney de Andrade Martins
Institutions
- Michigan Technological University (US)
- Universidade Federal Fluminense (BR)
- Zhengzhou University of Light Industry (CN)
- Fudan University (CN)
- National Clinical Research (US)
- Zhongshan Hospital (CN)
Publication Details
- Journal
- Kardiologiia
- Published
- 2024-09-30
- DOI
- https://doi.org/10.18087/cardio.2024.9.n2685
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 0.00