{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:50:00Z","timestamp":1791474600656,"version":"4.3.4"},"reference-count":60,"publisher":"Walter de Gruyter GmbH","issue":"4-5","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_bmt-2026-0192_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop and evaluate an automated deep learning framework for scoliosis detection from spinal radiographs and to compare the performance of a single-stage YOLOv11-based approach with a two-stage ROI-guided ResNet model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0192_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A publicly available spinal X-ray dataset was analyzed for key radiographic features, including spinal alignment, lateral curvature, vertebral asymmetry, and overall spinal contour. Two automated strategies were compared: a YOLOv11-based framework for spinal region detection and direct classification, and a two-stage approach in which the spinal region of interest was first localized using bounding box regression and then classified with ResNet.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0192_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The YOLOv11-based framework outperformed the ROI-based ResNet approach in distinguishing scoliosis from non-scoliosis radiographs. It captured scoliosis-related deformity patterns more effectively and demonstrated stronger diagnostic stability across representative cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0192_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The YOLOv11-based framework demonstrated superior performance over the two-stage ResNet strategy for automated scoliosis recognition and may serve as a useful adjunct for radiographic screening.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/bmt-2026-0192","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T14:58:01Z","timestamp":1782745081000},"page":"519-535","source":"Crossref","is-referenced-by-count":1,"title":["Spinal x-ray based scoliosis diagnosis using deep learning: a comparison of YOLOv11 and ResNet"],"prefix":"10.1515","volume":"71","author":[{"given":"Hongbo","family":"Zhang","sequence":"first","affiliation":[{"name":"First Clinical Medical College, Anhui Medical University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialong","family":"Qi","sequence":"additional","affiliation":[{"name":"Department of Spine , Third Affiliated Hospital of Anhui Medical University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Spine , Third Affiliated Hospital of Anhui Medical University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"2026100814512968358_j_bmt-2026-0192_ref_001","doi-asserted-by":"crossref","unstructured":"Weinstein, SL, Dolan, LA, Cheng, JCY, Danielsson, A, Morcuende, JA. 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