{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:54:35Z","timestamp":1781877275673,"version":"3.54.5"},"reference-count":14,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62331008"],"award-info":[{"award-number":["62331008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62276040"],"award-info":[{"award-number":["62276040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Doctoral Innovation Project of the First Affiliated Hospital of Chongqing Medical University","award":["2024MD754029"],"award-info":[{"award-number":["2024MD754029"]}]},{"name":"Doctoral Innovation Project of the First Affiliated Hospital of Chongqing Medical University","award":["CSTB2021TIAD-KPX0060"],"award-info":[{"award-number":["CSTB2021TIAD-KPX0060"]}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"crossref","award":["CSTB2024NSCQ-MSX1218"],"award-info":[{"award-number":["CSTB2024NSCQ-MSX1218"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"crossref","award":["CSTB2023NSCQ-LZX0047"],"award-info":[{"award-number":["CSTB2023NSCQ-LZX0047"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"crossref","award":["CSTB2024TIAD-KPX0040"],"award-info":[{"award-number":["CSTB2024TIAD-KPX0040"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Medical Research Project of Chongqing Municipal Health Commission","award":["2019GDRC001"],"award-info":[{"award-number":["2019GDRC001"]}]},{"name":"Technology Innovation and Application Development Key Projects of Chongqing","award":["CSTB2025TIAD-KPX0009"],"award-info":[{"award-number":["CSTB2025TIAD-KPX0009"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Thoracolumbar fractures (T11\u2013L2) are common spinal injuries, and accurate classification is essential for treatment planning. However, diagnosis remains challenging because of complex fracture morphology and variability in clinical experience. This study evaluated the feasibility of a deep learning framework for automated classification of thoracolumbar fractures using CT images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective study was conducted using CT images from 596 patients with thoracolumbar fractures. A convolutional neural network based on a Multiple Instance Learning (MIL) framework was developed, using AlexNet with a gated attention mechanism for feature aggregation. Separate models were trained for coronal (Z-type) and axial (S-type) views, and an ensemble strategy combined the two predictions. Model performance was compared with that of interns and senior physicians.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The S-type model achieved a classification accuracy of 75.2%, while the Z-type model achieved an accuracy of 88.0%. The ensemble model reached an overall accuracy of 83.8%. With assistance from the deep learning model, the diagnostic accuracy of interns improved from 74% to 95%, approaching the performance of senior physicians (98%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed deep learning framework showed promising performance for automated classification of thoracolumbar fractures and may serve as a decision-support tool to improve diagnostic efficiency.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/14604582261462739","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:51:22Z","timestamp":1781740282000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning framework for automated classification of thoracolumbar fractures using spinal CT images"],"prefix":"10.1177","volume":"32","author":[{"given":"Xiao","family":"Qu","sequence":"first","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucheng","family":"Shu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huazhang","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dagang","family":"Tang","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningdao","family":"Li","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3783-9438","authenticated-orcid":false,"given":"Xiaoji","family":"Luo","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Chongqing Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,6,17]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/sc.2010.130"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.1097\/BRS.0b013e3182a8a381"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00586-016-4623-0"},{"key":"e_1_3_6_5_2","first-page":"570","article-title":"A framework for multiple-instance learning","volume":"10","author":"Maron O","year":"1997","unstructured":"Maron O, Lozano-P\u00e9rez T. A framework for multiple-instance learning. Advances in neural information processing systems 1997; 10: 570-576.","journal-title":"Advances in neural information processing systems"},{"key":"e_1_3_6_6_2","unstructured":"Zheng C Zang M Hong X et al. Automating in-network machine learning. arXiv preprint arXiv:2205.08824. 2022."},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30115-8_9"},{"key":"e_1_3_6_8_2","unstructured":"Detyniecki M. Fundamentals on Aggregation Operators. PhD thesis. Berkeley CA: University of California Berkeley 2001."},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(94)00224-U"},{"issue":"89","key":"e_1_3_6_10_2","first-page":"1","article-title":"Neural operator: Learning maps between function spaces with applications to pdes","volume":"24","author":"Kovachki N","year":"2023","unstructured":"Kovachki N, Li Z, Liu B, et al. Neural operator: Learning maps between function spaces with applications to pdes. 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