Deep learning of echocardiography distinguishes between presence and absence of late gadolinium enhancement on cardiac magnetic resonance in patients with hypertrophic cardiomyopathy
Compared to the reference model solely based on clinical parameters, our new model integrating the reference model and deep learning-based analysis of echocardiographic images demonstrated superiority in distinguishing LGE on CMR in patients with HCM. The novel deep learning-based method can be used as an assistive technology to facilitate the decision-making process of performing CMR with gadolinium enhancement.
Authors
- Keitaro Akita (ORCID: https://orcid.org/0000-0001-5395-1887)
- Kenya Kusunose (ORCID: https://orcid.org/0000-0002-4909-754X)
- Kohei Hasegawa (ORCID: https://orcid.org/0000-0002-5739-7999)
- Akihiro Haga (ORCID: https://orcid.org/0000-0002-6717-2726)
- Matthew J. Maurer (ORCID: https://orcid.org/0000-0002-1867-0526)
- Yuichi J. Shimada (ORCID: https://orcid.org/0000-0002-3494-307X)
- Yoshitaka Kosaka
- Michael A. Fifer (ORCID: https://orcid.org/0000-0002-8363-445X)
- K. Ishiyama
- Toshiyasu Shimomura
Institutions
- Harvard University (US)
- Columbia University Irving Medical Center (US)
- University of the Ryukyus (JP)
- Massachusetts General Hospital (US)
- Tokushima University Hospital (JP)
- Tokushima University (JP)
Publication Details
- Journal
- Echo Research and Practice
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1186/s44156-024-00059-8
- Citations
- 8
- Primary Topic
- Cardiomyopathy and Myosin Studies
- Type
- article
- Field-Weighted Citation Impact
- 3.22
Funders
- American Heart Association
- Bristol-Myers Squibb
- Pfizer
- Alnylam Pharmaceuticals
- Prothena
- Moderna
- Feldstein Medical Foundation
- Korea Institute of Oriental Medicine
- Novo Nordisk
- National Institutes of Health
- Irving Medical Center, Columbia University