An automatic and real-time echocardiography quality scoring system based on deep learning to improve reproducible assessment of left ventricular ejection fraction
This study demonstrates that artificial intelligence-assisted echocardiography scoring system aligns well with manual quality scoring. Through the supervision of real-time echocardiogram quality, the artificial intelligence model can assist doctors in providing more reproducible and consistent assessments of cardiac function.
Authors
- Yitao Jiang
- Lisi Liao
- Kai Yi Wu (ORCID: https://orcid.org/0000-0001-6546-2191)
- Yuan Zhu
- Xiaowei Xu
- Xiaoshan Li (ORCID: https://orcid.org/0000-0002-4316-5660)
- Chen Cui (ORCID: https://orcid.org/0000-0002-8996-6623)
- Hongwen Fei (ORCID: https://orcid.org/0000-0003-3731-2668)
- Alice Meng
- Bobo Shi
Institutions
- Guangdong Medical College (CN)
- Shanghai American School (CN)
- Shenzhen Metro (China) (CN)
- Chinese University of Hong Kong, Shenzhen (CN)
- Guangdong Academy of Medical Sciences (CN)
- Guangdong Provincial People's Hospital (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-12-31
- DOI
- https://doi.org/10.21037/qims-24-512
- Citations
- 2
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- Guangdong Provincial People's Hospital
- MicroPort
- Basic and Applied Basic Research Foundation of Guangdong Province