An enhanced UHMWPE wear particle detection approach based on YOLOv9

IF 2.3 4区医学 Q3 ENGINEERING, BIOMEDICAL

Medical Engineering & Physics Pub Date : 2025-06-06 DOI:10.1016/j.medengphy.2025.104377

Lingmeng Li , Mingzhen Deng , Steven Su , Richard M. Hall , J.L. Tipper

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引用次数: 0

Abstract

Ultra-high molecular weight polyethylene (UHMWPE) has been widely used in total joint arthroplasty for orthopedic and spinal implants. However, the biological response to UHMWPE wear particles has been identified as a major contributor to inflammatory synovitis and periprosthetic osteolysis, which could lead to aseptic loosening and long-term implant failure. Traditional manual detection and classification of UHMWPE wear particles are labor-intensive, time-consuming, and prone to human error, which requires the development of automated detection techniques.

This study proposes a novel deep learning-based framework for detecting UHMWPE wear particles, utilizing high-resolution field emission gun-scanning electron microscopy (FEG-SEM) images. The proposed approach employs an enhanced YOLOv9 object detection model, incorporating programmable gradient information (PGI) and generalized efficient layer aggregation networks (GELAN) to improve the localization and detection accuracy of small objects. Additionally, a customized Focal Loss function is integrated to address class imbalance and enhance sensitivity to submicron and nanoscale wear particles.

Experimental evaluations demonstrate that our proposed model achieves a mean average precision (mAP) of 84.0%, outperforming the baseline YOLOv5 model by 7.7%. Furthermore, compared to mainstream object detection models such as YOLOv8 and Faster R-CNN, our approach exhibits superior detection accuracy and robustness, particularly in identifying wear particles in complex backgrounds and overlapping regions.

In addition to developing an advanced detection algorithm, this study establishes a dedicated and expert-annotated UHMWPE wear particle dataset, addressing a critical gap in orthopedic implant research. The proposed framework provides a scalable, high-precision, and cost-effective solution for the automated detection of UHMWPE wear particles, supporting improved implant monitoring, osteolysis prevention, and clinical decision-making in orthopedic and spinal implant evaluations.

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基于YOLOv9的增强UHMWPE磨损颗粒检测方法

超高分子量聚乙烯（UHMWPE）已广泛应用于骨科和脊柱植入物的全关节置换术中。然而，对UHMWPE磨损颗粒的生物反应已被确定为炎症性滑膜炎和假体周围骨溶解的主要因素，这可能导致无菌性松动和长期植入失败。传统的UHMWPE磨损颗粒的人工检测和分类劳动强度大，耗时长，容易出现人为错误，需要发展自动化检测技术。本研究提出了一种新的基于深度学习的框架，用于检测超高分子量聚乙烯磨损颗粒，利用高分辨率场发射枪扫描电子显微镜（fg - sem）图像。该方法采用改进的YOLOv9目标检测模型，结合可编程梯度信息（PGI）和广义高效层聚合网络（GELAN），提高了小目标的定位和检测精度。此外，还集成了定制的焦损功能，以解决类不平衡问题，提高对亚微米和纳米级磨损颗粒的灵敏度。实验评估表明，我们提出的模型达到了84.0%的平均精度（mAP），比基线YOLOv5模型高出7.7%。此外，与主流的目标检测模型（如YOLOv8和Faster R-CNN）相比，我们的方法具有更高的检测精度和鲁棒性，特别是在识别复杂背景和重叠区域的磨损颗粒方面。除了开发先进的检测算法外，本研究还建立了一个专用的专家注释的UHMWPE磨损颗粒数据集，解决了骨科植入物研究中的一个关键空白。提出的框架为UHMWPE磨损颗粒的自动检测提供了可扩展、高精度和经济高效的解决方案，支持改进的植入物监测、骨溶解预防以及骨科和脊柱植入物评估的临床决策。

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

Medical Engineering & Physics 工程技术-工程：生物医学

CiteScore

4.30

自引率

4.50%

发文量

172

审稿时长

3.0 months

期刊介绍： Medical Engineering & Physics provides a forum for the publication of the latest developments in biomedical engineering, and reflects the essential multidisciplinary nature of the subject. The journal publishes in-depth critical reviews, scientific papers and technical notes. Our focus encompasses the application of the basic principles of physics and engineering to the development of medical devices and technology, with the ultimate aim of producing improvements in the quality of health care.Topics covered include biomechanics, biomaterials, mechanobiology, rehabilitation engineering, biomedical signal processing and medical device development. Medical Engineering & Physics aims to keep both engineers and clinicians abreast of the latest applications of technology to health care.