Clinical biomechanics (Bristol, Avon) · 2025
AI assisted diagnosis using DEANet to improve correct diagnosis of iliac wing fracture and ischial spine fracture.
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Tsai Chun-Hao, Lin Kai-Cheng, Chen Yen-Yu, Chen Po-Chia, Lo Yuan-Shun, Ho Tsung-Yu
Аннотация
BACKGROUND: Hip fractures-particularly those involving the iliac wing and ischial spine-are complex injuries that often require CT or MRI scans for accurate diagnosis. However, these imaging modalities are costly, time-consuming, and involve exposure to radiation or contrast-related risks. This study aims to develop an AI-based classification model, the Dynamic Efficient Attention Network, capable of automatically distinguishing between iliac wing and ischial spine fractures using pelvic X-ray images. The objective is to facilitate early diagnosis and reduce reliance on advanced imaging modalities.
METHODS: The proposed method employs a dual-branch architecture that integrates EfficientNet-B0 with an Enhanced Depth-wise Separable Attention Block to enhance edge-region feature representation. The model was trained using pelvic X-ray images collected from China Medical University Hospital and evaluated based on accuracy, precision, recall, F1 score, and Intersection over Union.
FINDINGS: The model achieved an accuracy of 85 % on the test dataset and demonstrated robust performance across all evaluation metrics. These findings suggest that the proposed method has the potential to function as a reliable AI-assisted diagnostic tool for the early and accurate classification of hip fractures, thereby supporting clinical decision-making and improving treatment planning.
INTERPRETATION: Compared to existing approaches that rely on CT or MRI imaging, the proposed method demonstrates that advanced processing of X-ray images can yield clinically meaningful classification results. This underscores the potential of the proposed method as a cost-effective, efficient, and accessible diagnostic tool, especially in settings where access to advanced imaging modalities is limited.
METHODS: The proposed method employs a dual-branch architecture that integrates EfficientNet-B0 with an Enhanced Depth-wise Separable Attention Block to enhance edge-region feature representation. The model was trained using pelvic X-ray images collected from China Medical University Hospital and evaluated based on accuracy, precision, recall, F1 score, and Intersection over Union.
FINDINGS: The model achieved an accuracy of 85 % on the test dataset and demonstrated robust performance across all evaluation metrics. These findings suggest that the proposed method has the potential to function as a reliable AI-assisted diagnostic tool for the early and accurate classification of hip fractures, thereby supporting clinical decision-making and improving treatment planning.
INTERPRETATION: Compared to existing approaches that rely on CT or MRI imaging, the proposed method demonstrates that advanced processing of X-ray images can yield clinically meaningful classification results. This underscores the potential of the proposed method as a cost-effective, efficient, and accessible diagnostic tool, especially in settings where access to advanced imaging modalities is limited.