Dual-Stream Attention-Based Classification Network for Tibial Plateau Fractures via Diffusion Model Augmentation and Segmentation Map Integration.

Abstract

Objective: This study aimed to explore a novel method that integrates the segmentation guidance classification and the diffusion model augmentation to realize the automatic classification for tibial plateau fractures (TPFs).

Methods: YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital. Additionally, a segmentation-guided classification approach was proposed. To enhance the dataset, a diffusion model was further demonstrated for data augmentation.

Results: The novel method that integrated the segmentation-guided classification and diffusion model augmentation significantly improved the accuracy and robustness of fracture classification. The average accuracy of classification for TPFs rose from 0.844 to 0.896. The comprehensive performance of the dual-stream model was also significantly enhanced after many rounds of training, with both the macro-area under the curve (AUC) and the micro-AUC increasing from 0.94 to 0.97. By utilizing diffusion model augmentation and segmentation map integration, the model demonstrated superior efficacy in identifying Schatzker I, achieving an accuracy of 0.880. It yielded an accuracy of 0.898 for Schatzker II and III and 0.913 for Schatzker IV; for Schatzker V and VI, the accuracy was 0.887; and for intercondylar ridge fracture, the accuracy was 0.923.

Conclusion: The dual-stream attention-based classification network, which has been verified by many experiments, exhibited great potential in predicting the classification of TPFs. This method facilitates automatic TPF assessment and may assist surgeons in the rapid formulation of surgical plans.