Frontal plane mechanical leg alignment estimation from knee x-rays using deep learning

Abstract

ObjectiveLower limb malalignment can complicate symptoms and accelerate knee osteoarthritis (OA), necessitating consideration in study population selection. In this study, we develop and validate a deep learning model that classifies leg alignment as “normal” or “malaligned” from knee antero-posterior (AP)/postero-anterior (PA) radiographs alone, using an adjustable hip-knee-ankle (HKA) angle threshold. Material and MethodsWe utilized 8,878 digital radiographs, including 6,181 AP/PA full-leg x-rays (LLRs) and 2,697 AP/PA knee x-rays (2,292 with positioning frame, 405 without). The model’s evaluation involved two steps: In step 1, the model’s predictions on knee images cropped from LLRs were compared against the ground truth from the original LLRs. In step 2, the model was tested on knee AP radiographs, using corresponding same-day LLRs as a proxy for ground truth. ResultsThe model effectively classified alignment, with step one achieving sensitivity and specificity of 0.92 for a threshold of 7.5°, and 0.90 and 0.85 for 5°. For positioning frame images, step two showed a sensitivity of 0.85 and specificity of 0.81 for 7.5°, and 0.79 and 0.74 for 5°. For non-positioning frame images, sensitivity and specificity were 0.91 and 0.83 for 7.5°, and 0.9 and 0.86 for 5°. ConclusionThe model developed in this study accurately classifies lower limb malalignment from AP/PA knee radiographs using adjustable thresholds, offering a practical alternative to LLRs. This can enhance the precision of study population selection and patient management.