Two-centers machine learning analysis for predicting acid-fast bacilli results in tuberculosis sputum tests
We developed a diagnostic model using multiple ML methods to predict AFB results, achieving satisfactory diagnostic performance.
Authors
- Chengqian Huang (ORCID: https://orcid.org/0000-0001-6681-7548)
- Yong Zhao
- Xinli Zhan (ORCID: https://orcid.org/0000-0002-6936-481X)
- Jichong Zhu (ORCID: https://orcid.org/0000-0003-3701-5620)
- Shaofeng Wu (ORCID: https://orcid.org/0009-0006-5576-3162)
- Tianyou Chen (ORCID: https://orcid.org/0000-0002-8668-6650)
- Chenxing Zhou
Institutions
- Sun Yat-sen University (CN)
- Guilin Medical University (CN)
- Guilin University of Aerospace Technology (CN)
- First Affiliated Hospital of GuangXi Medical University (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- 181st Hospital of Chinese People's Liberation Army (CN)
- Affiliated Hospital of Guilin Medical College (CN)
Publication Details
- Journal
- Journal of Clinical Tuberculosis and Other Mycobacterial Diseases
- Published
- 2025-01-27
- DOI
- https://doi.org/10.1016/j.jctube.2025.100511
- Citations
- 3
- Primary Topic
- COVID-19 diagnosis using AI
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- National Natural Science Foundation of China