Objective To assess the effect of medial distal femoral osteotomy combined with interlocking nailing on the treatment of knee osteoarthritis with valgus deformity. Methods From May 1996 toAugust 2000, 16 patients with knee osteoarthritis accompanied by valgus deformity were treated by medial wedged distal femoral osteotomy combined with interlocking nailing. Full-length radiographs were taken before operation and 8 weeks and 2 years after operation. The parameters, including the femorotibial angle, the tibial angle, the femoral angle, the femoral condyletibial plateau angle, and the lateral joint space, were measured by these radiographs. The function of knee was evaluated by the 100 point rating scale standard of knee. Results The mean postoperative score was significantly improved from 50.4±15.9 points to 78.5±12.9 points 2 years after the surgery. The lateraljoint space was increased from 2.1±1.8 mm to 4.7±1.7 mm and the femoral condyletibial angle decreased from 5.6±2.9° to 1.6±3.4°. There were complications in 2 cases: 1 case of delayed union and 1 case of superficial wound infection. Conclusion Medial distal femoral osteotomy combined with interlocking nailing proves to be an effective approach to treat knee osteoarthritis with valgus deformity.
To facilitate the early intelligent screening of pediatric genu valgum, this study develops a deep learning–based gait recognition model tailored for clinical application. The model is constructed upon a three-dimensional residual network architecture and incorporates a triplet attention module alongside a spatial hierarchical pooling module, jointly enhancing feature interaction across temporal, spatial, and channel dimensions. This design ensures an optimal balance between representational capacity and computational efficiency. Evaluated on a self-constructed dataset, the model achieves precision of 98.0%, 97.1%, and 96.5%, recall rates of 97.5%, 97.0%, and 95.0%, and F1-scores of 0.98, 0.97, and 0.96 on the training, validation, and test sets, respectively, demonstrating excellent recognition performance and strong generalization ability. Ablation experiments confirm the importance of the proposed model’s core components in improving performance, and comparative experiments further highlight its significant advantages in recognition accuracy and robustness. Visualization experiments reveal that the model effectively focuses on key regions of gait images, with attention regions aligning closely with clinical anatomical landmarks, thereby enhancing the interpretability of the model’s decision-making in clinical applications. In summary, the proposed model not only offers an efficient and reliable technical solution for early intelligent screening of genu valgum in children, but also provides a practical pathway for applying gait recognition technology in medical diagnosis.
