Chest computed tomography-based artificial intelligence-aided latent class analysis for diagnosis of severe pneumonia
A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Authors
- Xuee Cui
- Caiting Chu (ORCID: https://orcid.org/0000-0002-9243-2876)
- Meijiao Jiang
- Zhenghai Lu
- Chengjin Gao (ORCID: https://orcid.org/0000-0002-6867-8024)
- Siwei Lu
- Ting Gui (ORCID: https://orcid.org/0000-0003-1439-6272)
- Shuhui Zhao
- Yiran Guo (ORCID: https://orcid.org/0000-0001-6303-1216)
- Wenhua Li (ORCID: https://orcid.org/0000-0002-9616-8080)
Institutions
- Shanghai Jiao Tong University (CN)
- XinHua Hospital (CN)
Publication Details
- Journal
- Chinese Medical Journal
- Published
- 2024-11-06
- DOI
- https://doi.org/10.1097/cm9.0000000000003226
- Citations
- 1
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- National Natural Science Foundation of China