Communication Dans Un Congrès Année : 2024

Post-treatment gait prediction after botulinum toxin injections using deep learning with an attention mdechanism

Résumé

Neurological conditions often manifest as gait disorders, frequently linked to spasticity. Botulinum Toxin Type A (BTX-A) injections commonly treat spasticity-related gait issues. Achieving optimal treatment outcomes with a favourable benefit-risk ratio remains crucial. This paper proposes an innovative method to predict post-BTX-A treatment knee and ankle kinematics by leveraging pre-treatment data and treatment specifics. Our approach relies on a bidirectional long-short-term memory (Bi-LSTM) network integrated with an attention mechanism within a deep learning architecture. The primary objective is to assess the efficacy of this methodology in accurately forecasting gait cycle kinematics for the knee and ankle joints after BTX-A intervention. Two deep learning models are designed, integrating categorical medical treatment data (MTD) representing injected muscles: (1) embedded within the Bi-LSTM network’s hidden layers and (2) through a gating mechanism. These architectures aim to model interactions among various treatment combinations when multiple muscles are injected simultaneously. Through comparative analysis with state-of-the-art approaches, our study demonstrates that incorporating attention mechanisms yields superior results. The average root-mean-squared error for predictions stands at 3.03∘ (R2 = 0.87) for knee kinematics and 2.18∘ (R2 = 0.83) for ankle kinematics. Our findings conclusively indicate that our proposed approach surpasses existing methods, offering higher predictive accuracy for post-BTX-A treatment kinematics.

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hal-05022515 , version 1 (23-07-2025)

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Adil Khan, Omar Galarraga, Sonia Garcia-Salicetti, Vincent Vigneron. Post-treatment gait prediction after botulinum toxin injections using deep learning with an attention mdechanism. 10th International Conference on Machine Learning, Optimization, and Data Science (LOD), Giuseppe Nicosia; Varun Ojha; Sven Giesselbach; M. Panos Pardalos; Renato Umeton, Sep 2024, Castiglione della Pescaia, Italy. pp.163-177, ⟨10.1007/978-3-031-82481-4_12⟩. ⟨hal-05022515⟩
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