Deep learning radiomics of left atrial appendage features for predicting atrial fibrillation recurrence
LAA morphology and volume are strongly linked to AF RFCA outcomes. We developed an LAA segmentation network and a predictive model that combines DL radiomics and clinical variables to estimate the probability of AF recurrence.
Authors
- Weili Ge
- Wenting Lin (ORCID: https://orcid.org/0000-0002-1374-3087)
- Sixiang Jia (ORCID: https://orcid.org/0000-0003-3177-3391)
- Ziwen Wang (ORCID: https://orcid.org/0009-0005-1552-3976)
- Jing Zheng (ORCID: https://orcid.org/0000-0003-2790-1143)
- Shudong Xia (ORCID: https://orcid.org/0000-0001-6228-5783)
- Chao Feng (ORCID: https://orcid.org/0000-0003-0155-9159)
- Wei Wang (ORCID: https://orcid.org/0000-0002-6822-8162)
- Jiangbo Lin (ORCID: https://orcid.org/0000-0001-9168-3553)
- Yin Yanping
Institutions
- Zhejiang International Studies University (CN)
- Wenzhou Medical University (CN)
- Zhejiang Taizhou Hospital (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-05-26
- DOI
- https://doi.org/10.1186/s12880-025-01740-y
- Citations
- 1
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- National Natural Science Foundation of China
- Science and Technology Plan Project of Taizhou