Prediction of post-Schroth Cobb angle changes in adolescent idiopathic scoliosis patients based on neural networks and surface electromyography.

Abstract

Introduction: To develop a temporal-convolutional-LSTM (TCN-LSTM) hybrid model integrating surface electromyography (sEMG) signals for forecasting post-Schroth Cobb angle progression in adolescent idiopathic scoliosis (AIS) patients, thereby offering accurate feedback for personalized treatment.

Methodology: A total of 143 AIS patients were included. A systematic Schroth exercise training program was designed. sEMG data from specific muscles and Cobb angle measurements were collected. A neural network model integrating Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM) layers, and feature vectors was constructed. Four prediction models were compared: TCN-LSTM hybrid model, TCN, LSTM, and Support Vector Regression (SVR).

Results: The TCN-LSTM hybrid model demonstrated superior performance, with Cobb angle-Thoracic (Cobb Angle-T) prediction accuracy reaching R² = 0.63 (baseline) and 0.69 (Week 24), achieving overall R² = 0.74. For Cobb angle-Lumbar (Cobb Angle-L), accuracy was R² = 0.61 (baseline) and 0.65 (Week 24), with overall R² = 0.73. The SVR model showed lowest performance (R² < 0.12).

Conclusion: The TCN-LSTM hybrid model can precisely predict Cobb angle changes in AIS patients during Schroth exercises, especially in long-term predictions. It provides real-time feedback for clinical treatment and contributes to optimizing treatment plans, presenting a novel prediction approach and reference basis for evaluating the effectiveness of Schroth correction exercises in AIS patients.