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Dual-Stream Attention-Based Classification Network for Tibial Plateau Fractures via Diffusion Model Augmentation and
Yi Xie1,2, Zhi-Wei Hao3, Xin-Meng Wang4
1Department of Orthopedics Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Current Medical Science
|February 25, 2025
Summary
A new method combining segmentation-guided classification and diffusion model augmentation improves tibial plateau fracture (TPF) classification accuracy. This approach enhances automated fracture assessment and aids surgical planning.
Area of Science:
- Orthopedic surgery
- Medical imaging analysis
- Artificial intelligence in healthcare
Background:
- Tibial plateau fractures (TPFs) require accurate classification for effective treatment.
- Current classification methods can be time-consuming and subjective.
- Automated classification systems are needed to improve efficiency and accuracy.
Purpose of the Study:
- To develop and evaluate a novel method for automatic classification of TPFs.
- To integrate segmentation guidance and diffusion model augmentation for enhanced classification.
- To improve the accuracy and robustness of TPF classification.
Main Methods:
- Utilized YOLOv8n-cls for baseline model construction on a dataset of 3781 patients.
- Proposed a segmentation-guided classification approach.
- Employed a diffusion model for data augmentation to enhance the dataset.
Main Results:
- The integrated method significantly improved TPF classification accuracy from 0.844 to 0.896.
- The dual-stream model's comprehensive performance improved, with macro-AUC and micro-AUC increasing from 0.94 to 0.97.
- High accuracy achieved for specific fracture types: Schatzker I (0.880), II-III (0.898), IV (0.913), V-VI (0.887), and intercondylar ridge (0.923).
Conclusions:
- The dual-stream attention-based classification network shows significant potential for TPF classification.
- This automated method can assist surgeons in rapid surgical plan formulation.
- The approach facilitates efficient and accurate TPF assessment.
