Comparative analysis of existing literature.

Bone fracture diagnosis is a critical aspect of sports medicine, where accurate and timely detection enables effective treatment and rapid recovery. This study proposes Deep-Fed, a federated deep learning framework for fracture diagnosis in athletes. Deep-Fed integrates convolutional neural networks with a specialized classification module, FractureNet, and trains it across distributed athletic clinics using federated averaging without exchanging raw images, thereby preserving patient privacy while leveraging diverse data sources. The framework was evaluated on three benchmark datasets—Deep-I, Deep-II, and Deep-III—representing varied imaging conditions and patient groups. Deep-Fed achieved accuracy rates of 96.23 ± 0.42%, 97.11 ± 0.35%, and 96.73 ± 0.39%, respectively, significantly outperforming Baseline 1 (87.23 ± 0.68%), Baseline 2 (90.15 ± 0.55%), and Baseline 3 (94.49 ± 0.47%). Statistical analysis using paired t-tests confirmed that Deep-Fed’s improvements were significant (p

骨折诊断是运动医学的核心环节之一，精准且及时的检测可助力患者实现有效治疗与快速康复。本研究提出了面向运动员骨折诊断的联邦深度学习框架Deep-Fed。该框架将卷积神经网络（convolutional neural networks）与专用分类模块FractureNet相结合，并通过联邦平均（federated averaging）算法在分布式运动医学诊所间完成模型训练，全程无需交换原始影像数据，既保护了患者隐私，又充分利用了多源数据资源。该框架在三组基准数据集Deep-I、Deep-II与Deep-III上开展了评估，这三组数据集覆盖了不同的成像条件与患者群体。Deep-Fed在三组数据集上分别取得了96.23 ± 0.42%、97.11 ± 0.35%与96.73 ± 0.39%的准确率，显著优于基准模型Baseline 1（87.23 ± 0.68%）、Baseline 2（90.15 ± 0.55%）与Baseline 3（94.49 ± 0.47%）。采用配对t检验进行的统计分析证实，Deep-Fed的性能提升具有统计学显著性（p