{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T07:06:06Z","timestamp":1784099166958,"version":"3.55.0"},"reference-count":52,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52575299"],"award-info":[{"award-number":["52575299"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52205276"],"award-info":[{"award-number":["52205276"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82572697"],"award-info":[{"award-number":["82572697"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["YG2024QNB08"],"award-info":[{"award-number":["YG2024QNB08"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["YG2026QNB10"],"award-info":[{"award-number":["YG2026QNB10"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioeng. Biotechnol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The study\u2019s objective is to propose a novel non-invasive method for rapid screening and regular assessment of adolescent idiopathic scoliosis (AIS) through development of a wearable system integrated with multiple inertial measurement units (IMUs) and deep learning models. The system is designed to automatically distinguish between healthy individuals and AIS patients, and subsequently predict the Cobb angle based on continuous temporal kinematic angular sequences acquired during gait.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Gait kinematic data were acquired from 124 participants (104 patients with average Cobb angle of 21.62 \u00b1 7.93\u00b0 and 20 healthy subjects) using a 9-IMU wearable device. Extracted angular features were analyzed to quantify bilateral asymmetry, compare differences across severity subgroups, and evaluate their linear correlation with Cobb angles. A two-stage deep learning framework was implemented with a convolutional neural network (CNN) classification model developed for the rapid screening of scoliosis, and a CNN-Transformer model was designed and compared with other five model architectures to predict Cobb angles from the acquired temporal angular sequences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Scapular kinematics emerged as the most prominent marker of asymmetry, and knee joint kinematics served as the strongest indicator of severity. Meanwhile, angular features of knee, hip and ankle joints demonstrated weak negative linear correlations with Cobb angle. In addition, the scoliosis screening model achieved high predictive performance, with an accuracy of 96.59% and a precision-recall AUC of 0.94 for scoliosis detection. For Cobb angle prediction, the CNN-Transformer model regularized with Gaussian noise during training proved most effective, yielding a mean absolute error of 2.14 \u00b1 0.28\u00b0 and\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      value of 0.85 \u00b1 0.03, outperforming other architectural alternatives evaluated in this study.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Kinematic analysis of angular data validated the efficacy of the wearable system and effectively captured gait characteristics specific to AIS. The deep learning models accurately distinguished scoliosis patients from healthy cases and predicted Cobb angles using temporal kinematic angular sequences, providing a safe, non-invasive, operator-friendly approach suitable for rapid screening and regular assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fbioe.2026.1857499","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T06:11:42Z","timestamp":1784095902000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning based screening and regular assessment of adolescent idiopathic scoliosis using wearable IMU sensors"],"prefix":"10.3389","volume":"14","author":[{"given":"Xinyang","family":"Tan","sequence":"first","affiliation":[{"name":"School of Design, Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanwen","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Design, Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baohong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Design, Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Shanghai Children\u2019s Hospital","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Renji Hospital","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyuan","family":"Song","sequence":"additional","affiliation":[{"name":"School of Design, Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Design, Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Design, Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhedong","family":"Shan","sequence":"additional","affiliation":[{"name":"School of Design, Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bosi","family":"Li","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University","place":["Pittsburgh, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengxuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Design, Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyang","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Design, Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qichao","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Shanghai Children\u2019s Hospital","place":["Shanghai, China"]},{"name":"Department of Pediatric Orthopedics, Bozhou People\u2019s Hospital","place":["Bozhou, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, Renji Hospital","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2115","DOI":"10.1007\/s00586-022-07245-4","article-title":"A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation","volume":"31","author":"Alukaev","year":"2022","journal-title":"Eur. 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