The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model
The pre-existing emphysema phenotype of COPD severely aggravated the pulmonary involvement of COVID-19 patients. Deep learning and radiomics features may provide more information to accurately predict the nucleic acid turning-negative time, which is expected to play an important role in clinical practice.
Authors
- Haina Li (ORCID: https://orcid.org/0000-0003-2160-7621)
- Wenxiu Zhang (ORCID: https://orcid.org/0000-0001-9962-1998)
- Xiaoyun Liang (ORCID: https://orcid.org/0000-0002-1851-3408)
- Yongquan Hou (ORCID: https://orcid.org/0000-0002-9945-8649)
- Jiaqi Zhao (ORCID: https://orcid.org/0000-0002-3564-5090)
- Lu Wang (ORCID: https://orcid.org/0000-0001-6336-5806)
- Mengzhou Sun (ORCID: https://orcid.org/0009-0001-2201-1910)
- Xiaoguang Yang (ORCID: https://orcid.org/0000-0001-6599-8631)
- Yanhui Liu (ORCID: https://orcid.org/0000-0001-9835-6475)
Institutions
- Inner Mongolia Medical College Hospital (CN)
- Neusoft (China) (CN)
- Hohhot First Hospital (CN)
- Inner Mongolia Medical University (CN)
Publication Details
- Journal
- BMC Pulmonary Medicine
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1186/s12890-024-03333-x
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00