{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:15:22Z","timestamp":1789085722142,"version":"build-2803163510"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioeng. Biotechnol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methodology<\/jats:title><jats:p>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).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The TCN-LSTM hybrid model demonstrated superior performance, with Cobb angle-Thoracic (Cobb Angle-T) prediction accuracy reaching R<jats:sup>2<\/jats:sup> = 0.63 (baseline) and 0.69 (Week 24), achieving overall R<jats:sup>2<\/jats:sup> = 0.74. For Cobb angle-Lumbar (Cobb Angle-L), accuracy was R<jats:sup>2<\/jats:sup> = 0.61 (baseline) and 0.65 (Week 24), with overall R<jats:sup>2<\/jats:sup> = 0.73. The SVR model showed lowest performance (R<jats:sup>2<\/jats:sup> &amp;lt; 0.12).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fbioe.2025.1570022","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T05:38:53Z","timestamp":1747201133000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Prediction of post-Schroth Cobb angle changes in adolescent idiopathic scoliosis patients based on neural networks and surface electromyography"],"prefix":"10.3389","volume":"13","author":[{"given":"Shuguang","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"f2508","DOI":"10.1136\/bmj.f2508","article-title":"Adolescent idiopathic scoliosis","volume":"346","author":"Altaf","year":"2013","journal-title":"BMJ Clin. 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