UK-YOLOv10: Deep Learning-Based Detection of Surgical Instruments

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2025-06-27 DOI:10.1002/rcs.70083

Li Zhang, Guanqun Guo, Wenjie Wang

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引用次数: 0

Abstract

Background

Accurate detection of surgical instruments is essential for robot-assisted surgery. Existing methods face challenges in both accuracy and real-time performance, limiting their clinical applicability.

Methods

We propose UK-YOLOv10, a novel framework that integrates two innovations: the uni-fusion attention module (UFAM) for enhanced multi-scale feature representation, and the C2fKAN module, which employs KAN convolution to improve classification accuracy and accelerate training.

Results

On the M2CAI16-Tool-Locations dataset, UK-YOLOv10 achieves detection accuracy of 96.7%, an [email protected] of 96.4%, and an [email protected]:0.95 of 0.605, outperforming YOLOv10 by 3%, 2.2% and 3.6%, respectively. Generalisation on COCO2017 resulted in an [email protected]:0.95 of 0.386.

Conclusion

UK-YOLOv10 significantly improves surgical instrument detection and demonstrates strong potential for robot-assisted surgeries.

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基于深度学习的手术器械检测

手术器械的准确检测是机器人辅助手术的关键。现有方法在准确性和实时性方面都面临挑战，限制了其临床适用性。我们提出了一个新的框架UK-YOLOv10，它集成了两个创新：用于增强多尺度特征表示的统一融合注意模块（UFAM）和使用KAN卷积来提高分类精度和加速训练的C2fKAN模块。结果在M2CAI16-Tool-Locations数据集上，UK-YOLOv10的检测准确率为96.7%，[email protected]的检测准确率为96.4%，[email protected]的检测准确率为0.95(0.605)，分别优于YOLOv10 3%、2.2%和3.6%。对COCO2017的概括得出[email protected]:0.95（0.386）。结论UK-YOLOv10显著提高了手术器械的检测水平，在机器人辅助手术中具有很强的应用潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.