OrthoDETR: A Streamlined Transformer-Based Approach for Precision Detection of Orthopedic Medical Devices

Author:

Zhang Xiaobo1,Li Huashun2,Li Jingzhao2,Zhou Xuehai1

Affiliation:

1. School of Computer Science and Technology, University of Science and Technology of China, Hefei 230001, China

2. College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

Abstract

The rapid and accurate detection of orthopedic medical devices is pivotal in enhancing health care delivery, particularly by improving workflow efficiency. Despite advancements in medical imaging technology, current detection models often fail to meet the unique requirements of orthopedic device detection. To address this gap, we introduce OrthoDETR, a Transformer-based object detection model specifically designed and optimized for orthopedic medical devices. OrthoDETR is an evolution of the DETR (Detection Transformer) model, with several key modifications to better serve orthopedic applications. We replace the ResNet backbone with the MLP-Mixer, improve the multi-head self-attention mechanism, and refine the loss function for more accurate detections. In our comparative study, OrthoDETR outperformed other models, achieving an AP50 score of 0.897, an AP50:95 score of 0.864, an AR50:95 score of 0.895, and a frame per second (FPS) rate of 26. This represents a significant improvement over the DETR model, which achieved an AP50 score of 0.852, an AP50:95 score of 0.842, an AR50:95 score of 0.862, and an FPS rate of 20. OrthoDETR not only accelerates the detection process but also maintains an acceptable performance trade-off. The real-world impact of this model is substantial. By facilitating the precise and quick detection of orthopedic devices, OrthoDETR can potentially revolutionize the management of orthopedic workflows, improving patient care, and enhancing the efficiency of healthcare systems. This paper underlines the significance of specialized object detection models in orthopedics and sets the stage for further research in this direction.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science

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