Deep‐learning based multibeat echocardiographic cardiac phase detection
EchoPhaseNet exhibits clear advantages over existing studies in data annotation and collection costs, as well as detection speed, and is applicable to a wider range of detection scenarios. It demonstrates good practicality and promising prospects for clinical multibeat echocardiographic cardiac phase detection.
Authors
- Zhongqing Shi
- Zhanru Qi
- Zhenlan Jin (ORCID: https://orcid.org/0000-0001-9601-4877)
- Ruiyang Chen (ORCID: https://orcid.org/0000-0002-1538-1702)
- Chunjie Shan (ORCID: https://orcid.org/0009-0007-1179-3674)
- Shouhua Luo (ORCID: https://orcid.org/0000-0002-3307-1652)
- Guanjun Guo (ORCID: https://orcid.org/0000-0001-8783-0020)
- Aijuan Fang
- Yue Du
- Heng-Da Cheng (ORCID: https://orcid.org/0000-0003-3569-7621)
- Xiaoxian Wang (ORCID: https://orcid.org/0009-0004-3058-768X)
- Jing Yao (ORCID: https://orcid.org/0000-0002-8867-3079)
- S. Qian
Institutions
- Nanjing Drum Tower Hospital (CN)
- Jiangsu Province Hospital (CN)
- Southeast University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-03-19
- DOI
- https://doi.org/10.1002/mp.17733
- Citations
- 1
- Primary Topic
- Cardiovascular Function and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- National Natural Science Foundation of China
- Jiangsu Provincial Key Research and Development Program