He Shichun, Wei Meiqi, Meng Deyu, Lv Zongnan, Guo Hongzhi, Yang Guang, Wang Ziheng
Division of Computational Biology, Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, Changchun, 130024, Jilin, China.
Department of Adolescent Physical Health, Chinese Center of Exercise Epidemiology, Northeast Normal University, Renmin Street, Changchun, 130024, Jilin, China.
Comput Biol Med. 2024 Dec;183:109214. doi: 10.1016/j.compbiomed.2024.109214. Epub 2024 Oct 8.
Genu valgum (GV), a prevalent postural deformity in adolescents, is traditionally diagnosed using methods that are complex, costly, and accompanied by radiation risks. To address these challenges, we evaluated 1519 Chinese adolescents, collecting GV annotations from three medical professionals to establish a robust dataset. Leveraging these annotations, we developed an end-to-end GV prediction model using RTMpose for body landmark extraction from images. However, a key challenge was the inaccuracy of landmarks, which adversely affects downstream tasks. To mitigate this, we harnessed the parallels between pose estimation biases and adversarial perturbations, implementing adversarial training to bolster model robustness against noisy landmark data. Our model achieved a significant improvement, with an accuracy of 75%, compared to the baseline's 64.25%. These results underscore the model's efficacy as a high-performance, non-contact GV detection method and demonstrate the effectiveness of adversarial training in enhancing landmark-related tasks, providing a safer, cost-effective alternative for adolescent GV diagnosis.
膝外翻（GV）是青少年中一种常见的姿势畸形，传统上使用复杂、昂贵且有辐射风险的方法进行诊断。为应对这些挑战，我们评估了1519名中国青少年，从三名医学专业人员那里收集膝外翻标注，以建立一个强大的数据集。利用这些标注，我们开发了一个端到端的膝外翻预测模型，使用RTMpose从图像中提取身体关键点。然而，一个关键挑战是关键点的不准确，这对下游任务产生了不利影响。为了缓解这一问题，我们利用姿势估计偏差和对抗性扰动之间的相似性，实施对抗训练以增强模型对有噪声关键点数据的鲁棒性。与基线的64.25%相比，我们的模型取得了显著改进，准确率达到75%。这些结果强调了该模型作为一种高性能、非接触式膝外翻检测方法的有效性，并证明了对抗训练在增强与关键点相关任务方面的有效性，为青少年膝外翻诊断提供了一种更安全、成本效益更高的替代方法。
