Li Lingmeng, Deng Mingzhen, Su Steven, Hall Richard M, Tipper J L
Faculty of Engineering and IT, University of Technology, Sydney, NSW, Australia.
Faculty of Engineering and IT, University of Technology, Sydney, NSW, Australia; College of Artificial Intelligence and Big Data for Medical Science, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China.
Med Eng Phys. 2025 Aug;142:104377. doi: 10.1016/j.medengphy.2025.104377. Epub 2025 Jun 6.
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.
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。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原因，这可能导致无菌性松动和长期植入失败。传统的手动检测和分类UHMWPE磨损颗粒既费力又耗时，而且容易出现人为错误，因此需要开发自动化检测技术。本研究提出了一种基于深度学习的新型框架，用于检测UHMWPE磨损颗粒，该框架利用高分辨率场发射枪扫描电子显微镜（FEG-SEM）图像。所提出的方法采用了增强型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），以提高小目标的定位和检测精度。此外，还集成了定制的焦点损失函数，以解决类别不平衡问题，并提高对亚微米和纳米级磨损颗粒的敏感性。实验评估表明，我们提出的模型平均精度均值（mAP）达到84.0%，比基线YOLOv5模型高出7.7%。此外，与YOLOv8和Faster R-CNN等主流目标检测模型相比，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中的磨损颗粒方面。除了开发先进的检测算法外，本研究还建立了一个专门的、经过专家注释的UHMWPE磨损颗粒数据集，填补了骨科植入物研究中的一个关键空白。所提出的框架为UHMWPE磨损颗粒的自动化检测提供了一种可扩展、高精度且经济高效的解决方案，有助于改善骨科和脊柱植入物评估中的植入物监测、骨溶解预防和临床决策。
