Integrated radiomics and deep learning model for identifying medullary sponge kidney stones.
This study introduces an innovative approach for MSK stone diagnosis by integrating radiomics and deep learning features. The proposed model offers high diagnostic accuracy and promising clinical utility.
Authors
- Zheng Xu (ORCID: https://orcid.org/0000-0003-0311-7004)
- Rui Xu (ORCID: https://orcid.org/0000-0003-0458-9548)
- Bo Xiao (ORCID: https://orcid.org/0000-0002-6161-9478)
- Weiguo Hu (ORCID: https://orcid.org/0000-0001-7420-6640)
- Bixiao Wang
- Jianxing Li (ORCID: https://orcid.org/0000-0002-9291-2351)
- Dan Luo (ORCID: https://orcid.org/0009-0003-9141-1752)
- Yubao Liu (ORCID: https://orcid.org/0000-0001-5807-6487)
- Haifeng Song (ORCID: https://orcid.org/0000-0001-8581-0085)
- Gang Zhang (ORCID: https://orcid.org/0009-0001-2680-2146)
Institutions
- Beijing Tsinghua Chang Gung Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- PubMed
- Published
- 2025-01-01
- DOI
- https://doi.org/10.3389/fmed.2025.1623850
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00