Feasibility validation of automatic diagnosis of mitral valve prolapse from multi-view echocardiographic sequences based on deep neural network
This study pioneers the integration of artificial intelligence into MVP diagnosis and demonstrates the effectiveness of deep neural networks in overcoming the challenges of traditional diagnostic methods. The efficiency and accuracy of the proposed automated approach suggest its potential for clinical applications in the diagnosis of valvular heart disease.
Authors
- Yongshi Wang (ORCID: https://orcid.org/0000-0002-7851-1623)
- Zhengdan Ge
- Chunqiang Hu (ORCID: https://orcid.org/0000-0001-5825-2241)
- Yixiu Liang (ORCID: https://orcid.org/0000-0001-5467-8958)
- Cuizhen Pan (ORCID: https://orcid.org/0000-0001-7697-9970)
- Wufeng Xue (ORCID: https://orcid.org/0000-0002-9776-0975)
- Dehong Kong (ORCID: https://orcid.org/0000-0002-2296-3209)
- Dong Ni
- Weipeng Zhao
- Yumeng Xing (ORCID: https://orcid.org/0000-0001-7791-2313)
- Zhenyi Ge (ORCID: https://orcid.org/0000-0001-6563-6403)
- Haiyan Chen (ORCID: https://orcid.org/0000-0001-5273-2770)
- Zijian Wu
- Lili Dong (ORCID: https://orcid.org/0000-0002-2222-3147)
- Xianhong Shu (ORCID: https://orcid.org/0009-0007-1887-5672)
Institutions
- Sun Yat-sen University (CN)
- Shenzhen University (CN)
- Fudan University (CN)
- Huadong Hospital (CN)
- Zhongshan Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
Publication Details
- Journal
- European Heart Journal - Imaging Methods and Practice
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1093/ehjimp/qyae086
- Citations
- 1
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- National Natural Science Foundation of China
- Fudan University
- Natural Science Foundation of Guangdong Province
- Zhongshan Hospital
- Shenzhen Fundamental Research Program