An enhanced UHMWPE wear particle detection approach based on YOLOv9

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.