Phase-Based Gait Prediction after Botulinum Toxin Treatment Using Deep Learning

Abstract

Gait disorders in neurological diseases are frequently associated with spasticity. Intramuscular injection of Botulinum Toxin Type A (BTX-A) can be used to treat spasticity. Providing optimal treatment with the highest possible benefit–risk ratio is a crucial consideration. This paper presents a novel approach for predicting knee and ankle kinematics after BTX-A treatment based on pre-treatment kinematics and treatment information. The proposed method is based on a Bidirectional Long Short-Term Memory (Bi-LSTM) deep learning architecture. Our study’s objective is to investigate this approach’s effectiveness in accurately predicting the kinematics of each phase of the gait cycle separately after BTX-A treatment. Two deep learning models are designed to incorporate categorical medical treatment data corresponding to the injected muscles: (1) within the hidden layers of the Bi-LSTM network, (2) through a gating mechanism. Since several muscles can be injected during the same session, the proposed architectures aim to model the interactions between the different treatment combinations. In this study, we conduct a comparative analysis of our prediction results with the current state of the art. The best results are obtained with the incorporation of the gating mechanism. The average prediction root mean squared error is 2.99° (R2 = 0.85) and 2.21° (R2 = 0.84) for the knee and the ankle kinematics, respectively. Our findings indicate that our approach outperforms the existing methods, yielding a significantly improved prediction accuracy.

Keywords

• botulinum toxin
• clinical gait analysis
• deep learning
• gait rehabilitation
• long short-term memory
• multi-task learning