Gait prediction post-botulinum toxin injection: A transfer learning approach

Accurate prediction of post-treatment gait outcomes in children with cerebral palsy (CP) remains a major challenge in clinical practice. Deep learning offers promising opportunities to model complex kinematic changes, particularly following botulinum toxin (BT) injections. This study aimed to evaluate the ability of two neural network architectures, a bidirectional long short-term memory (Bi-LSTM) and a one-dimensional convolutional neural network (1D CNN), to predict post-BT gait kinematics from pre-treatment data. Gait data from children with CP were analyzed using both models, trained either from scratch or with transfer learning. Performance was assessed using root mean square error (RMSE) across joint angles, planes of motion, and individual subjects. Statistical comparisons were performed with Wilcoxon tests. Bi-LSTM and 1D CNN models achieved comparable levels of accuracy, with average root mean square error (RMSE) values around 8-9°. Transfer learning significantly improved model performance across error- and correlation-based metrics, with especially pronounced improvements for 1D CNN model (RMSE=9.81°from scratch ; RMSE=7.39°with fine-tuning) in clinically relevant joint movements such as hip and knee flexion/extension. These findings highlight the potential of deep learning models not only to capture complex gait patterns but also to adapt to treatment-induced changes. These models could support treatment planning, improve patient counseling, and contribute to the development of personalized rehabilitation strategies. Future work should expand to larger, multicenter datasets and emphasize clinical interpretability to facilitate translation into practice.