## 1 Introduction  
Orthopedic medical devices play a pivotal role in the treatment and management of various musculoskeletal disorders [1, 2, 3]. Accurate and efficient detection of these devices is paramount for meticulous management during the workflow process. This, in turn, can significantly enhance the overall efficiency and safety of orthopedic treatments [4]. Existing object detection models, dominantly those based on convolutional neural networks (CNNs), have shown promising results across different application domains [5, 6, 7]. However, they often fall short when applied to orthopedic medical device detection due to their inability to address the specificity and complexity of medical imaging data and the variability in device appearances. The conventional CNN-based models are not optimally designed to handle these challenges, highlighting a pressing need for more robust and adaptive approaches. Acknowledging the current limitations in orthopedic medical device detection using deep learning models, we introduce OrthoDETR, a specialized Transformer-based object detection strategy meticulously designed for the orthopedic domain. The traditional DEtection TRansformer (DETR) model, despite its strengths, suffers from certain inefficienciesand inadequacies when applied to orthopedics, necessitating improvements specifically tailored to this context [8]. For instance, the ResNet backbone [9], while generally competent, is outperformed by the MLP-Mixer [10] on tasks such as image classification, due to the latter's higher computational efficiency and lower parameter count. As such, replacing ResNet with MLP-Mixer in our model would likely lead to enhanced performance and efficiency. Similarly, the standard multi-head self-attention mechanism central to the Transformer model comes with high computational complexity, particularly problematic when handling long sequences. By optimizing this mechanism, we aim to reduce computational burdens without compromising the ability to capture rich contextual information. Lastly, traditional loss functions like cross-entropy loss may not be suitable for dealing with more complex orthopedic tasks, such as those involving class imbalances or requiring precise probability predictions. With an improved loss function, OrthoDETR can better tackle these challenges, further boosting model performance. Through these strategic modifications to the DETR architecture, OrthoDETR promises significant advancements in the field of orthopedic medical device detection and broader medical imaging analysis. The primary contributions of this study can be delineated as follows:  
1. We introduce a unique object detection strategy, termed as OrthoDETR, that is specifically designed for the efficient and accurate identification of orthopedic medical devices. It counters the limitations of existing models and operates effectively against the challenges emerged from intricate medical imaging data and varied device appearances. 2. OrthoDETR extends the core architecture of DETR (Detection Transformer) and incorporates several significant innovations to better accommodate the orthopedic domain. These key enhancements involve substituting the ResNet backbone with an MLP-Mixer for superior feature extraction, refining the multi-head self-attention mechanism for enhanced context comprehension, and adjusting the loss function for optimized model training. 3. Through rigorous experimentation, we demonstrate that OrthoDETR provides considerable improvements in detection speed, while only resulting in a slight decrease in performance. This makes it a valuable tool for detecting orthopedic medical devices, particularly in the context of fine-grained management during workflow processes. For further clarity, the remainder of this paper is structured as follows: Section 2 offers a concise overview of the existing work in object detection and orthopedic medical device detection. Section 3 elaborates on the proposed OrthoDETR model, including its detailed architecture and optimization strategies.