Artificial Intelligence-Enhanced Analysis of Echocardiography-Based Radiomic Features for Myocardial Hypertrophy Detection and Etiology Differentiation
This study confirms that artificial intelligence-enhanced echocardiography-based radiomics effectively differentiate the etiology of LVH, highlighting the potential of artificial intelligence-driven texture and geographic analysis in LVH evaluation.
Authors
- Hong‐Mi Choi (ORCID: https://orcid.org/0000-0002-6856-2486)
- In‐Chang Hwang (ORCID: https://orcid.org/0000-0003-4966-3924)
- Youngtaek Hong (ORCID: https://orcid.org/0000-0003-2104-5905)
- Inki Moon (ORCID: https://orcid.org/0000-0002-3404-3012)
- Yeonyee Elizabeth Yoon (ORCID: https://orcid.org/0000-0002-8479-9889)
- Dawun Jeong (ORCID: https://orcid.org/0000-0001-9791-9555)
- Yeonggul Jang (ORCID: https://orcid.org/0000-0002-5805-7494)
- Hyuk‐Jae Chang (ORCID: https://orcid.org/0000-0002-6139-7545)
- Jina Lee
- Seung-Ah Lee (ORCID: https://orcid.org/0000-0001-6964-8356)
- Jaeik Jeon (ORCID: https://orcid.org/0000-0002-9737-3449)
- Jiyeon Kim (ORCID: https://orcid.org/0009-0009-3419-9955)
- Goo-Yeong Cho
- Sihyeon Jeong
Institutions
- Yonsei University (KR)
- Soonchunhyang University (KR)
- University Health System (US)
- Soonchunhyang University Hospital Seoul (KR)
- Seoul National University Bundang Hospital (KR)
- JW Pharmaceutical (South Korea) (KR)
- Yonsei University Health System (KR)
- Bucheon University (KR)
Publication Details
- Journal
- Circulation Cardiovascular Imaging
- Published
- 2025-03-27
- DOI
- https://doi.org/10.1161/circimaging.124.017436
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.16