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OrthoDetNet: An Enhanced YOLO-Based Framework for Detection of Orthopedic Surgical Instruments.

Peng Xu, Guangquan Zhou, Mengxing Liu

IEEE Journal of Biomedical and Health Informatics

|August 20, 2025

Summary

This study introduces OrthoDetNet, a novel method for detecting orthopedic surgical instruments, addressing challenges like occlusion and similar shapes. The developed system achieves effective detection, crucial for surgical procedures and robotics.

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Area of Science:

Computer Vision
Medical Robotics
Surgical Technology

Background:

Accurate surgical instrument detection is vital for patient safety and advancing surgical robotics.
A significant gap exists in dedicated datasets and detection methods for orthopedic surgical instruments.
Challenges include size variation, shape similarity, and severe occlusions in orthopedic surgical environments.

Purpose of the Study:

To develop and evaluate an effective deep learning-based method for detecting orthopedic surgical instruments.
To address the specific challenges of size variation, shape similarity, and occlusion in orthopedic instrument detection.
To create a novel dataset for orthopedic surgical instrument detection.

Main Methods:

Proposed OrthoDetNet method with three modules: FilterUnit for occlusion mitigation, DEUnit for enhancing feature discrimination, and BDFusion for multi-scale feature fusion.
Developed a new dataset using proximal femoral nail antirotation (PFNA) instruments in a controlled environment.
Utilized adaptive feature filtering, fine-grained feature enhancement, and bi-directional feature fusion.

Main Results:

OrthoDetNet demonstrated effectiveness in detecting orthopedic surgical instruments on the created dataset.
The individual modules (FilterUnit, DEUnit, BDFusion) contributed to the overall performance improvement.
Experimental results validated the proposed method's capability in challenging scenarios.

Conclusions:

OrthoDetNet provides a robust solution for orthopedic surgical instrument detection, overcoming key challenges.
The developed dataset and method advance research in surgical robotics and computer-assisted surgery.
The proposed approach shows promise for real-world applications in surgical navigation and automation.