Adversarially trained RTMpose: A high-performance, non-contact method for detecting Genu valgum in adolescents

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.