Yu Lei, Zhang Shuai, Zhang Xueting, Wang Heng, You Mengnan, Jiang Yimin
Department of School Mathematics and Computer, Wuhan Polytechnic University, Wuhan, China.
Curr Med Imaging. 2025 Jun 20. doi: 10.2174/0115734056360714250612080450.
BACKGROUND: Knee osteoarthritis (KOA) is a degenerative joint disease commonly assessed using X-ray images based on the Kellgren-Lawrence (KL) criteria. Although the KL standard exists, its ambiguity often causes patients to misunderstand their condition, leading to overtreatment or delayed treatment and challenges in guiding precise surgical decisions. Moreover, the data-driven technology has been impeded by low resolution and feature distribution inconsistency of knee X-ray images. The imbalances between positive and negative samples further degrade detection accuracy. OBJECTIVE: The objective of this study was to develop a deep learning-based model, namely Task-aligned Path Aggregation Feature Fusion For Knee Osteoarthritis Detection (TPAFFKnee), to improve KOA detection accuracy by addressing limitations in traditional methods. Its more accurate detection could help in terms of proper treatment for patients and precision in surgery by physicians. METHODS: We proposed the TPAFFKnee model based on the EfficientNetB4 network, which introduced a path aggregation network for better feature extraction and replaced Fully Convolutional Network (FCN) with task-aligned detection as the head. Additionally, the loss function was improved by replacing the original loss function with Efficient Intersection over Union Loss (EIoU Loss) to address the imbalance between positive and negative samples. RESULTS: The results showed that the model could accurately detect KOA categories and lesion locations based on the KL classification criteria, with a Mean Average Precision (mAP) of 93% on the Mendeley KOA dataset of 1650 knee osteoarthritis X-ray images from several hospitals. The mAP for the K2, K3, and K4 categories were 98.6%, 98.5%, and 99.6%, respectively. Compared with Faster R-CNN, SSD, RetinaNet, EfficientNetB4, and YOLOX, the proposed algorithm improved detection mAP by 14.3%, 12.4%, 15.3%, 22.7%, and 4.3%. CONCLUSION: This study emphasizes the importance of the EfficientNetB4 network in KOA detection. The TPAFFKnee model provides an effective solution for improving the accuracy of KOA detection and offers a promising approach for standardized KL classification in medical applications. Future research can integrate more clinical data while improving the overall landscape of healthcare delivery through data-driven automation solutions.
背景：膝关节骨关节炎（KOA）是一种退行性关节疾病，通常根据Kellgren-Lawrence（KL）标准使用X射线图像进行评估。尽管存在KL标准，但其模糊性常常导致患者对自身病情产生误解，从而导致过度治疗或治疗延误，并在指导精确的手术决策方面面临挑战。此外，数据驱动技术受到膝关节X射线图像分辨率低和特征分布不一致的阻碍。正负样本之间的不平衡进一步降低了检测准确性。 目的：本研究的目的是开发一种基于深度学习的模型，即用于膝关节骨关节炎检测的任务对齐路径聚合特征融合模型（TPAFFKnee），通过解决传统方法中的局限性来提高KOA检测准确性。其更准确的检测有助于为患者提供适当治疗，并帮助医生提高手术精度。 方法：我们提出了基于EfficientNetB4网络的TPAFFKnee模型，该模型引入了路径聚合网络以更好地提取特征，并将全卷积网络（FCN）替换为任务对齐检测作为头部。此外，通过用高效交并比损失（EIoU Loss）替换原始损失函数来改进损失函数，以解决正负样本之间的不平衡问题。 结果：结果表明，该模型能够根据KL分类标准准确检测KOA类别和病变位置，在来自多家医院的1650张膝关节骨关节炎X射线图像组成的Mendeley KOA数据集上，平均精度均值（mAP）为93%。K2、K3和K4类别的mAP分别为98.6%、98.5%和99.6%。与Faster R-CNN、SSD、RetinaNet、EfficientNetB4和YOLOX相比，所提出算法的检测mAP分别提高了14.3%、12.4%、15.3%、22.7%和4.3%。 结论：本研究强调了EfficientNetB4网络在KOA检测中的重要性。TPAFFKnee模型为提高KOA检测准确性提供了一种有效解决方案，并为医学应用中的标准化KL分类提供了一种有前景的方法。未来研究可以整合更多临床数据，并通过数据驱动的自动化解决方案改善整体医疗服务格局。
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