Texture analysis combined with machine learning in radiographs of the knee joint: potential to identify tibial plateau occult fractures
TA of knee joint radiography combined with ML has achieved high performance in identifying patients at risk of occult fractures of the tibial plateau. Considering both the model performance and computational complexity, the LASSO feature selection method combined with the logistic regression classifier yielded the best classification performance in this process.
Authors
- Fenghua Zou
- Jianguo Zeng (ORCID: https://orcid.org/0000-0003-0428-5990)
- Liang Dong (ORCID: https://orcid.org/0000-0003-1782-6848)
- Haoxi Chen
Institutions
- University of Electronic Science and Technology of China (CN)
- Sichuan Cancer Hospital (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2024-12-24
- DOI
- https://doi.org/10.21037/qims-24-799
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81