Fracture detection of distal radius using deep- learning-based dual-channel feature fusion algorithm.

Abstract

Purpose: Distal radius fracture is a common trauma fracture and timely preoperative diagnosis is crucial for the patient's recovery. With the rise of deep-learning applications in the medical field, utilizing deep-learning for diagnosing distal radius fractures has become a significant topic. However, previous research has suffered from low detection accuracy and poor identification of occult fractures. This study aims to design an improved deep-learning model to assist surgeons in diagnosing distal radius fractures more quickly and accurately.

Methods: This study, inspired by the comprehensive analysis of anteroposterior and lateral X-ray images by surgeons in diagnosing distal radius fractures, designs a dual-channel feature fusion network for detecting distal radius fractures. Based on the Faster region-based convolutional neural network framework, an additional Residual Network 50, which is integrated with the Deformable and Separable Attention mechanism, was introduced to extract semantic information from lateral X-ray images of the distal radius. The features extracted from the 2 channels were then combined via feature fusion, thus enriching the network's feature information. The focal loss function was also employed to address the sample imbalance problem during the training process.The selection of cases in this study was based on distal radius X-ray images retrieved from the hospital's imaging database, which met the following criteria: inclusion criteria comprised clear anteroposterior and lateral X-ray images, which were diagnosed as distal radius fractures by experienced radiologists. The exclusion criteria encompassed poor image quality, the presence of severe multiple or complex fractures, as well as non-adult or special populations (e.g., pregnant women). All cases meeting the inclusion criteria were labeled as distal radius fracture cases for model training and evaluation. To assess the model's performance, this study employed several metrics, including accuracy, precision, recall, area under the precision-recall curve, and intersection over union.

Results: The proposed dual-channel feature fusion network achieved an average precision (AP)50 of 98.5%, an AP75 of 78.4%, an accuracy of 96.5%, and a recall of 94.7%. When compared to traditional models, such as Faster region-based convolutional neural network, which achieved an AP50 of 94.1%, an AP75 of 70.6%, a precision of 91.1%, and a recall of 92.3%, our method shows notable improvements in all key metrics. Similarly, when compared to other classic object detection networks like You Only Look Once version 4 (AP50=95.2%, AP75=72.2 %, precision=91.2%, recall=92.4%) and You Only Look Once version 5s (AP50=95.1%, AP75=73.8%, precision=93.7%, recall=92.8%), the dual-channel feature fusion network outperforms them in precision, recall, and AP scores. These results highlight the superior accuracy and reliability of the proposed method, particularly in identifying both apparent and occult distal radius fractures, demonstrating its effectiveness in clinical applications where precise detection of subtle fractures is critical.

Conclusion: This study found that combining anteroposterior and lateral X-ray images of the distal radius as input for deep-learning algorithms can more accurately and efficiently identify distal radius fractures, providing a reference for research on distal radius fractures.