Teoh Yun Xin, Othmani Alice, Lai Khin Wee, Goh Siew Li, Usman Juliana
Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia; LISSI, Université Paris-Est Créteil, Vitry sur Seine, 94400, France.
LISSI, Université Paris-Est Créteil, Vitry sur Seine, 94400, France.
Comput Methods Programs Biomed. 2023 Dec;242:107807. doi: 10.1016/j.cmpb.2023.107807. Epub 2023 Sep 20.
Knee osteoarthritis (OA) is a debilitating musculoskeletal disorder that causes functional disability. Automatic knee OA diagnosis has great potential of enabling timely and early intervention, that can potentially reverse the degenerative process of knee OA. Yet, it is a tedious task, concerning the heterogeneity of the disorder. Most of the proposed techniques demonstrated single OA diagnostic task widely based on Kellgren Lawrence (KL) standard, a composite score of only a few imaging features (i.e. osteophytes, joint space narrowing and subchondral bone changes). However, only one key disease pattern was tackled. The KL standard fails to represent disease pattern of individual OA features, particularly osteophytes, joint-space narrowing, and pain intensity that play a fundamental role in OA manifestation. In this study, we aim to develop a multitask model using convolutional neural network (CNN) feature extractors and machine learning classifiers to detect nine important OA features: KL grade, knee osteophytes (both knee, medial fibular: OSFM, medial tibial: OSTM, lateral fibular: OSFL, and lateral tibial: OSTL), joint-space narrowing (medial: JSM, and lateral: JSL), and patient-reported pain intensity from plain radiography.
We proposed a new feature extraction method by replacing fully-connected layer with global average pooling (GAP) layer. A comparative analysis was conducted to compare the efficacy of 16 different convolutional neural network (CNN) feature extractors and three machine learning classifiers.
Experimental results revealed the potential of CNN feature extractors in conducting multitask diagnosis. Optimal model consisted of VGG16-GAP feature extractor and KNN classifier. This model not only outperformed the other tested models, it also outperformed the state-of-art methods with higher balanced accuracy, higher Cohen's kappa, higher F1, and lower mean squared error (MSE) in seven OA features prediction.
The proposed model demonstrates pain prediction on plain radiographs, as well as eight OA-related bony features. Future work should focus on exploring additional potential radiological manifestations of OA and their relation to therapeutic interventions.
膝关节骨关节炎（OA）是一种使人衰弱的肌肉骨骼疾病，会导致功能残疾。膝关节OA的自动诊断具有实现及时和早期干预的巨大潜力，这有可能逆转膝关节OA的退行性进程。然而，鉴于该疾病的异质性，这是一项繁琐的任务。大多数已提出的技术广泛基于Kellgren Lawrence（KL）标准展示单一的OA诊断任务，KL标准是仅由少数影像特征（即骨赘、关节间隙变窄和软骨下骨改变）组成的综合评分。然而，仅解决了一种关键疾病模式。KL标准未能体现个体OA特征的疾病模式，特别是在OA表现中起重要作用的骨赘、关节间隙变窄和疼痛强度。在本研究中，我们旨在开发一种使用卷积神经网络（CNN）特征提取器和机器学习分类器的多任务模型，以检测九个重要的OA特征：KL分级、膝关节骨赘（双侧膝关节，内侧腓骨：OSFM、内侧胫骨：OSTM、外侧腓骨：OSFL和外侧胫骨：OSTL）、关节间隙变窄（内侧：JSM和外侧：JSL）以及患者报告的来自X线平片的疼痛强度。
我们提出了一种通过用全局平均池化（GAP）层替换全连接层的新特征提取方法。进行了一项对比分析，以比较16种不同的卷积神经网络（CNN）特征提取器和三种机器学习分类器的效果。
实验结果揭示了CNN特征提取器在进行多任务诊断方面的潜力。最优模型由VGG16 - GAP特征提取器和KNN分类器组成。该模型不仅优于其他测试模型，在七个OA特征预测中，它还在平衡准确率、科恩kappa系数、F1值方面更高，均方误差（MSE）更低，优于现有最先进的方法。
所提出的模型展示了对X线平片上疼痛的预测以及八个与OA相关的骨性特征。未来的工作应专注于探索OA的其他潜在放射学表现及其与治疗干预的关系。
