{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:44:06Z","timestamp":1792799046269,"version":"4.5.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"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. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Accurate assessment of remaining growth is crucial for risk stratification and treatment planning in adolescent idiopathic scoliosis (AIS). Risser staging is widely used but demonstrates only moderate reproducibility in routine practice, particularly on standing full-spine radiographs where the iliac apophysis occupies a small area. This study aimed to develop and evaluate an interpretable, decision-oriented deep-learning model for automatic stratification of skeletal maturity as Risser 0\u20132 vs. 3\u20135 using routine full-spine radiographs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This single-center retrospective study included 875 standing posteroanterior full-spine radiographs from AIS patients aged 10\u201318 years. An expert consensus reference standard was established using a three-surgeon adjudication process. A predefined, rule-based pelvic region of interest was automatically extracted for model training. The data were split at the patient level into training, validation, and test sets (70%\/10%\/20%, stratified). Multiple convolutional neural network architectures were evaluated, and ResNet-18 was selected based on performance stability. Model interpretability was assessed using gradient-weighted class activation mapping (Grad-CAM). An independent clinical reader study was conducted on 50 sampled test cases, in which three spine surgeons completed two reading sessions (without and with model assistance) to assess agreement with the expert reference and reading time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      For binary stratification (Risser 0\u20132 vs. 3\u20135), the selected model achieved an area under the receiver operating characteristic curve (AUC) of 0.938, an accuracy of 0.875, and a Cohen's kappa of 0.748 on the independent test set. Grad-CAM visualizations indicated that model predictions focused on the iliac apophysis and adjacent ossification regions. In the reader study, compared with unaided reading, model assistance reduced the mean reading time by 9.7\u201311.8\u2005s per case across the three readers, from 34.7\u201343.2\u2005s to 25.0\u201331.8\u2005s (all\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001) and improved agreement with the expert reference, with the most pronounced gains observed among junior surgeons. Exploratory six-class Risser staging showed substantially lower performance, particularly for intermediate stages.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Using routine standing full-spine radiographs, an interpretable deep-learning model enabled clinically actionable Risser stratification and improved reading efficiency in an independent reader study, without the need for additional imaging. This approach may support the standardized assessment of growth potential in routine AIS management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2026.1832066","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T05:56:42Z","timestamp":1781675802000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["From routine full-spine radiographs to decision-oriented Risser stratification: an interpretable deep-learning approach"],"prefix":"10.3389","volume":"14","author":[{"given":"Zexi","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Minimally Invasive Spine and Precision Orthopedics, The First Affiliated Hospital of Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xinjiang Medical-Engineering Integration Technology Center, The First Affiliated Hospital of Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Minimally Invasive Spine and Precision Orthopedics, The First Affiliated Hospital of Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Xue","sequence":"additional","affiliation":[{"name":"Department of Minimally Invasive Spine and Precision Orthopedics, The First Affiliated Hospital of Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Minimally Invasive Spine and Precision Orthopedics, The First Affiliated Hospital of Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Minimally Invasive Spine and Precision Orthopedics, The First Affiliated Hospital of Xinjiang Medical University","place":["Urumqi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1016\/S0140-6736(08)60658-3","article-title":"Adolescent idiopathic scoliosis","volume":"371","author":"Weinstein","year":"2008","journal-title":"Lancet"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.2106\/00004623-198466070-00013","article-title":"The prediction of curve progression in untreated idiopathic scoliosis during growth","volume":"66","author":"Lonstein","year":"1984","journal-title":"J Bone Joint Surg 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