Model performance in external validation.

Knee osteoarthritis (KOA) is a leading cause of disability globally. Early and accurate diagnosis is paramount in preventing its progression and improving patients’ quality of life. However, the inconsistency in radiologists’ expertise and the onset of visual fatigue during prolonged image analysis often compromise diagnostic accuracy, highlighting the need for automated diagnostic solutions. In this study, we present an advanced deep learning model, OA-HybridCNN (OHC), which integrates ResNet and DenseNet architectures. This integration effectively addresses the gradient vanishing issue in DenseNet and augments prediction accuracy. To evaluate its performance, we conducted a thorough comparison with other deep learning models using five-fold cross-validation and external tests. The OHC model outperformed its counterparts across all performance metrics. In external testing, OHC exhibited an accuracy of 91.77%, precision of 92.34%, and recall of 91.36%. During the five-fold cross-validation, its average AUC and ACC were 86.34% and 87.42%, respectively. Deep learning, particularly exemplified by the OHC model, has greatly improved the efficiency and accuracy of KOA imaging diagnosis. The adoption of such technologies not only alleviates the burden on radiologists but also significantly enhances diagnostic precision.

膝关节骨关节炎（Knee osteoarthritis, KOA）是全球范围内导致残疾的主要诱因之一。早期精准诊断对于阻断疾病进展、提升患者生活质量至关重要。然而，放射科医师专业水平参差不齐，加之长期阅片引发的视觉疲劳，往往会降低诊断准确性，这凸显了自动化诊断方案的研发需求。本研究提出了一款融合残差神经网络（Residual Network, ResNet）与稠密神经网络（Dense Network, DenseNet）架构的先进深度学习模型——OA-HybridCNN（简称OHC）。该融合方案有效解决了稠密神经网络的梯度消失问题，并提升了预测准确率。为评估该模型的性能，本研究采用五折交叉验证与外部测试方案，将其与多款主流深度学习模型进行了全面对比。OHC模型在所有性能指标上均优于对照模型。在外部测试中，OHC模型的准确率达91.77%，精确率为92.34%，召回率为91.36%。在五折交叉验证中，其平均曲线下面积（Area Under Curve, AUC）与平均准确率（ACC）分别为86.34%与87.42%。以OHC模型为代表的深度学习技术，大幅提升了膝关节骨关节炎影像诊断的效率与准确性。此类技术的应用不仅能够减轻放射科医师的工作负担，还能显著提升诊断精准度。