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.

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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
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article

Texture analysis combined with machine learning in radiographs of the knee joint: potential to identify tibial plateau occult fractures

Fenghua Zou, Jianguo Zeng, Liang Dong, Haoxi Chen
2 citations
Quantitative Imaging in Medicine and Surgery
Radiomics and Machine Learning in Medical Imaging
0.81
article

Texture analysis combined with machine learning in radiographs of the knee joint: potential to identify tibial plateau occult fractures

Fenghua Zou, Jianguo Zeng, Liang Dong, Haoxi Chen
article en
2 citations

Abstract

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.

Quantitative Imaging in Medicine and SurgeryVol. 15(1)
University of Electronic Science and Technology of China (CN), Sichuan Cancer Hospital (CN)
Quality Education
Openalex Percentile: Top 24%
Radiomics and Machine Learning in Medical Imaging
0.81
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