{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T11:57:37Z","timestamp":1794484657199,"version":"4.9.1"},"reference-count":25,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,1]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automated measurement tools have the promise of rapid and consistent measurements, but existing tools are still limited to some degree by manual user-entry requirements. This study presents a novel artificial intelligence (AI) tool called SpinePose that automatically predicts spinopelvic parameters with high accuracy without the need for manual entry.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>SpinePose was trained and validated on 761 sagittal whole-spine radiographs to predict the sagittal vertical axis (SVA), pelvic tilt (PT), pelvic incidence (PI), sacral slope (SS), lumbar lordosis (LL), T1 pelvic angle (T1PA), and L1 pelvic angle (L1PA). A separate test set of 40 radiographs was labeled by four reviewers, including fellowship-trained spine surgeons and a fellowship-trained radiologist with neuroradiology subspecialty certification. Median errors relative to the most senior reviewer were calculated to determine model accuracy on test images. Intraclass correlation coefficients (ICCs) were used to assess interrater reliability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>SpinePose exhibited the following median (interquartile range) parameter errors: SVA 2.2 mm (2.3 mm) (p = 0.93), PT 1.3\u00b0 (1.2\u00b0) (p = 0.48), SS 1.7\u00b0 (2.2\u00b0) (p = 0.64), PI 2.2\u00b0 (2.1\u00b0) (p = 0.24), LL 2.6\u00b0 (4.0\u00b0) (p = 0.89), T1PA 1.1\u00b0 (0.9\u00b0) (p = 0.42), and L1PA 1.4\u00b0 (1.6\u00b0) (p = 0.49). Model predictions also exhibited excellent reliability at all parameters (ICC 0.91\u20131.0).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>SpinePose accurately predicted spinopelvic parameters with excellent reliability comparable to that of fellowship-trained spine surgeons and neuroradiologists. Utilization of predictive AI tools in spinal imaging can substantially aid in patient selection and surgical planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2024.1.spine231252","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T12:55:25Z","timestamp":1713272125000},"page":"88-96","source":"Crossref","is-referenced-by-count":27,"title":["Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters"],"prefix":"10.3171","volume":"41","author":[{"given":"Edward S.","family":"Harake","sequence":"first","affiliation":[{"name":"School of Medicine and Departments of"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph R.","family":"Linzey","sequence":"additional","affiliation":[{"name":"Neurosurgery,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Computational Medicine and Bioinformatics, and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rushikesh S.","family":"Joshi","sequence":"additional","affiliation":[{"name":"Neurosurgery,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark M.","family":"Zaki","sequence":"additional","affiliation":[{"name":"Neurosurgery,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaes C.","family":"Jones","sequence":"additional","affiliation":[{"name":"Neurosurgery,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siri Sahib S.","family":"Khalsa","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Wexner Medical Center, The Ohio State University, Columbus, Ohio; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John H.","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Medicine and Departments of"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zachary","family":"Wilseck","sequence":"additional","affiliation":[{"name":"Radiology, University of Michigan, Ann Arbor, Michigan;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob R.","family":"Joseph","sequence":"additional","affiliation":[{"name":"Neurosurgery,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Todd C.","family":"Hollon","sequence":"additional","affiliation":[{"name":"Neurosurgery,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Semmes Murphey Neurologic and Spine Institute, University of Tennessee, Memphis, Tennessee"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"issue":"14","key":"b1","series-title":"Spine (Phila Pa 1976)","first-page":"1611","article-title":"Radiographic analysis of sagittal plane alignment and balance in standing volunteers and patients with low back pain matched for age, sex, and size. 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PLoS One. 2019;14(7):e0219854."},{"issue":"5","key":"b23","series-title":"Eur Spine J","first-page":"951","article-title":"Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach","volume":"28","author":"Galbusera F","year":"2019","unstructured":"Galbusera F, Niemeyer F, Wilke HJ,  Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach. Eur Spine J. 2019;28(5):951\u2013960."},{"issue":"11","key":"b24","series-title":"J Clin Med","first-page":"1826","article-title":"Artificial intelligence for automatic measurement of sagittal vertical axis using ResUNet framework","volume":"8","author":"Weng CH","year":"2019","unstructured":"Weng CH, Wang CL, Huang YJ,  Artificial intelligence for automatic measurement of sagittal vertical axis using ResUNet framework. 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Sci Rep. 2021;11(1):7618."}],"container-title":["Journal of Neurosurgery: Spine"],"original-title":[],"link":[{"URL":"https:\/\/thejns.org\/view\/journals\/j-neurosurg-spine\/41\/1\/article-p88.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/thejns.org\/downloadpdf\/journals\/j-neurosurg-spine\/41\/1\/article-p88.xml","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/thejns.org\/downloadpdf\/journals\/j-neurosurg-spine\/41\/1\/article-p88.xml","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T08:19:30Z","timestamp":1733473170000},"score":1,"resource":{"primary":{"URL":"https:\/\/thejns.org\/view\/journals\/j-neurosurg-spine\/41\/1\/article-p88.xml"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,1]]},"references-count":25,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3171\/2024.1.spine231252","relation":{},"ISSN":["1547-5654"],"issn-type":[{"value":"1547-5654","type":"print"}],"subject":[],"published":{"date-parts":[[2024,7,1]]}}}