Radiomics and Clinical Features for Distinguishing Kidney Stone–Associated Urinary Tract Infection: A Comprehensive Analysis of Machine Learning Classification
This study showed the effectiveness of combining radiomics and clinical features from CT scans to identify KS-UTIs. A combined model using MLP exhibited strong classification abilities.
Authors
- Jianjuan Lu
- Lingrui Liu (ORCID: https://orcid.org/0000-0003-4015-2589)
- Yanyan Liu (ORCID: https://orcid.org/0000-0001-8959-9168)
- Qiang Chen (ORCID: https://orcid.org/0009-0000-7791-8598)
- Jiabin Li (ORCID: https://orcid.org/0000-0002-6539-1802)
- Kun Zhu
- Yi Yang (ORCID: https://orcid.org/0009-0006-4196-2187)
- Ning Yang (ORCID: https://orcid.org/0000-0002-3169-2922)
Institutions
- Anhui Medical University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
Publication Details
- Journal
- Open Forum Infectious Diseases
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1093/ofid/ofae581
- Citations
- 1
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.18
Funders
- National Natural Science Foundation of China
- Anhui Medical University
- China Primary Health Care Foundation
- Natural Science Foundation of Anhui Province