A new integrated machine learning model: application to improve the accuracy of predicting left atrial appendage thrombus in patients with non-valvular atrial fibrillation
The LR model provides a reliable non-invasive approach for LAAT screening in high-risk AF patients by integrating TTE features with clinical data, potentially reducing reliance on TEE.
Authors
- huanhuan huo
- Fang He (ORCID: https://orcid.org/0000-0002-4185-9079)
- Peipei Mai
- Hua Wang (ORCID: https://orcid.org/0000-0002-2605-5697)
- Xiaona Li (ORCID: https://orcid.org/0000-0003-1766-6442)
- Dan Zhou (ORCID: https://orcid.org/0000-0001-8678-8270)
- Yongxin Li (ORCID: https://orcid.org/0000-0001-5543-4242)
Institutions
- Luoyang Central Hospital Affiliated to Zhengzhou University (CN)
- Second Affiliated Hospital of Xi'an Jiaotong University (CN)
- Henan Medical University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-08-15
- DOI
- https://doi.org/10.3389/fmed.2025.1661696
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China