{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:16:05Z","timestamp":1788934565047,"version":"build-2803163510"},"reference-count":62,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,1]]},"abstract":"\n OBJECTIVE<\/jats:title>\n SpinePose was developed in 2024 as a novel artificial intelligence (AI) tool to automatically predict spinopelvic parameters with high accuracy and without the need for manual entry. The authors\u2019 published results demonstrated excellent performance comparable to a fellowship-trained spine surgeon with more than 15 years of experience. To date, there have not been any studies that have externally validated the performance of AI-based spinopelvic parameter measurement tools on data acquired from other institutions. To assess the generalizability of SpinePose, the authors report its performance on an external set of heterogeneous whole-spine scoliosis radiographs obtained from an outside institution.<\/jats:p>\n <\/jats:sec>\n \n METHODS<\/jats:title>\n SpinePose was trained\/validated on a dataset of 761 sagittal whole-spine scoliosis radiographs from a single institution, with expert-level performance on both whole-spine and lumbosacral radiographs. In this study, the existing SpinePose model was used for inference on a new set of 49 whole-spine radiographs acquired at a tertiary academic hospital located out of state. Externally acquired radiographs represented a diverse set of images, incorporating patients who had undergone instrumentation and those who had not, and a wide variety of fusion constructs including complex deformity patients. Predicted measures included sagittal vertical axis (SVA), pelvic tilt (PT), pelvic incidence (PI), lumbar lordosis (LL), and T1\u2013pelvic angle (T1PA). Predicted parameter values relative to ground-truth manual annotations were calculated to determine the model\u2019s accuracy.<\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n Of the 49 images, 35 (71.4%) had instrumentation compared with 51.0% and 57.5% in the original SpinePose training and testing sets, respectively. All 5 parameters in the external dataset were significantly different at baseline compared with the original test set (p < 0.01). SpinePose accurately predicted all 5 spinopelvic parameters without any statistically significant differences: SVA, 50.7 mm vs 52.3 mm (p = 0.85); PT, 27.6\u00b0 vs 30.5\u00b0 (p = 0.24); PI, 58.0\u00b0 vs 61.8\u00b0 (p = 0.17); LL, 40.4\u00b0 vs 42.4\u00b0 (p = 0.77); and T1PA, 24.8\u00b0 vs 28.0\u00b0 (p = 0.21) when comparing ground truth annotations with predicted values.<\/jats:p>\n <\/jats:sec>\n \n CONCLUSIONS<\/jats:title>\n SpinePose was able to accurately predict spinopelvic parameters on an external validation cohort that was generated independently from the images on which the model was trained and validated. This highlights the generalizability of SpinePose to be implemented on novel images from other institutions and geographic settings with high accuracy and minimal preprocessing. The implementation of AI tools more broadly will help standardize our ability to both deliver and provide spine care and assist with surgical treatment and management of spine patients.<\/jats:p>\n <\/jats:sec>","DOI":"10.3171\/2025.3.focus2574","type":"journal-article","created":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T05:55:07Z","timestamp":1748757307000},"page":"E10","source":"Crossref","is-referenced-by-count":8,"title":["Validation of a novel artificial intelligence model (SpinePose) to automatically and accurately predict spinopelvic parameters using scoliosis radiographs in an external cohort"],"prefix":"10.3171","volume":"58","author":[{"given":"Rushikesh S.","family":"Joshi","sequence":"first","affiliation":[{"name":"Department of Neurological Surgery, University of Michigan, Ann Arbor;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward S.","family":"Harake","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Michigan, Ann Arbor;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason J.","family":"Haselhuhn","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, University of Minnesota, Minneapolis, Minnesota;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph R.","family":"Linzey","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, University of Michigan, Ann Arbor;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaes C.","family":"Jones","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, University of Michigan, Ann Arbor;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark M.","family":"Zaki","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, University of Michigan, Ann Arbor;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kari","family":"Odland","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, University of Minnesota, Minneapolis, Minnesota;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zachary","family":"Wilseck","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan, Ann Arbor, Michigan; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob R.","family":"Joseph","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, University of Michigan, Ann Arbor;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David W.","family":"Polly","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery, University of Minnesota, Minneapolis, Minnesota;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Todd C.","family":"Hollon","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, University of Michigan, Ann Arbor;"}],"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":"4","key":"b1","series-title":"Nat Med","first-page":"828","article-title":"Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging","volume":"29","author":"Hollon T","year":"2023","unstructured":"Hollon T, Jiang C, Chowdury A, Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging. Nat Med. 2023;29(4):828\u2013832."},{"issue":"1","key":"b2","series-title":"Nat Med","first-page":"52","article-title":"Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks","volume":"26","author":"Hollon TC","year":"2020","unstructured":"Hollon TC, Pandian B, Adapa AR, Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat Med. 2020;26(1):52\u201358."},{"issue":"6","key":"b3","series-title":"Neurosurgery","first-page":"758","article-title":"Rapid automated analysis of skull base tumor specimens using intraoperative optical imaging and artificial intelligence","volume":"90","author":"Jiang C","year":"2022","unstructured":"Jiang C, Bhattacharya A, Linzey JR, Rapid automated analysis of skull base tumor specimens using intraoperative optical imaging and artificial intelligence. Neurosurgery. 2022;90(6):758\u2013767."},{"issue":"8045","key":"b4","series-title":"Nature","first-page":"439","article-title":"Foundation models for fast, label-free detection of glioma infiltration","volume":"637","author":"Kondepudi A","year":"2025","unstructured":"Kondepudi A, Pekmezci M, Hou X, Foundation models for fast, label-free detection of glioma infiltration. Nature. 2025;637(8045):439\u2013445."},{"issue":"9","key":"b5","series-title":"Nat Med","first-page":"1337","article-title":"Automated deep-neural-network surveillance of cranial images for acute neurologic events","volume":"24","author":"Titano JJ","year":"2018","unstructured":"Titano JJ, Badgeley M, Schefflein J, Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med. 2018;24(9):1337\u20131341."},{"issue":"13","key":"b6","series-title":"Spine (Phila Pa 1976)","first-page":"915","article-title":"Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value","volume":"44","author":"Ames CP","year":"2019","unstructured":"Ames CP, Smith JS, Pellis\u00e9 F, Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value. Spine (Phila Pa 1976). 2019;44(13):915\u2013926."},{"issue":"4","key":"b7","series-title":"Neurospine","first-page":"686","article-title":"Artificial intelligence for adult spinal deformity","volume":"16","author":"Joshi RS","year":"2019","unstructured":"Joshi RS, Haddad AF, Lau D, Ames CP. Artificial intelligence for adult spinal deformity. Neurospine. 2019;16(4):686\u2013694."},{"issue":"10","key":"b8","series-title":"Spine J","first-page":"1626","article-title":"Artificial intelligence for adult spinal deformity: current state and future directions","volume":"21","author":"Joshi RS","year":"2021","unstructured":"Joshi RS, Lau D, Ames CP. Artificial intelligence for adult spinal deformity: current state and future directions. Spine J. 2021;21(10):1626\u20131634."},{"issue":"4","key":"b9","series-title":"Neurospine","first-page":"637","article-title":"Artificial intelligence and the future of spine surgery","volume":"16","author":"Joshi RS","year":"2019","unstructured":"Joshi RS, Lau D, Ames CP. Artificial intelligence and the future of spine surgery. 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J Bone Joint Surg Am. 2019;101(4):338\u2013352."},{"issue":"19 Suppl","key":"b14","series-title":"Spine (Phila Pa 1976)","first-page":"S119","article-title":"The SRS classification for adult spinal deformity: building on the King\/Moe and Lenke classification systems","volume":"31","author":"Lowe T","year":"2006","unstructured":"Lowe T, Berven SH, Schwab FJ, Bridwell KH. The SRS classification for adult spinal deformity: building on the King\/Moe and Lenke classification systems. 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Neurosurgery. 2013;73(4):559\u2013568."},{"issue":"5","key":"b24","series-title":"Neurosurg Focus","first-page":"E18","article-title":"Complications and intercenter variability of three-column osteotomies for spinal deformity surgery: a retrospective review of 423 patients","volume":"36","author":"Bianco K","year":"2014","unstructured":"Bianco K, Norton R, Schwab F, Complications and intercenter variability of three-column osteotomies for spinal deformity surgery: a retrospective review of 423 patients. Neurosurg Focus. 2014;36(5):E18."},{"issue":"3","key":"b25","series-title":"J Neurosurg Spine","first-page":"297","article-title":"Comparison of perioperative complications following posterior column osteotomy versus posterior-based 3-column osteotomy for correction of rigid cervicothoracic deformity: a single-surgeon series of 95 consecutive cases","volume":"33","author":"Lau D","year":"2020","unstructured":"Lau D, Deviren V, Joshi RS, Ames CP. 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J Neurosurg Spine. 2020;33(5):588\u2013600."},{"issue":"1","key":"b27","series-title":"J Neurosurg Spine","first-page":"1","article-title":"Prospective multicenter assessment of perioperative and minimum 2-year postoperative complication rates associated with adult spinal deformity surgery","volume":"25","author":"Smith JS","year":"2016","unstructured":"Smith JS, Klineberg E, Lafage V, Prospective multicenter assessment of perioperative and minimum 2-year postoperative complication rates associated with adult spinal deformity surgery. 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J Neurosurg Spine. 2021;34(2):178\u2013189."},{"issue":"3","key":"b31","series-title":"Spine Deform","first-page":"755","article-title":"Spine surgeon versus AI algorithm full-length radiographic measurements: a validation study of complex adult spinal deformity patients","volume":"12","author":"Haselhuhn JJ","year":"2024","unstructured":"Haselhuhn JJ, Soriano PBO, Grover P, Spine surgeon versus AI algorithm full-length radiographic measurements: a validation study of complex adult spinal deformity patients. Spine Deform. 2024;12(3):755\u2013761."},{"issue":"4","key":"b1","series-title":"Nat Med","first-page":"828","article-title":"Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging","volume":"29","author":"Hollon T","year":"2023","unstructured":"Hollon T, Jiang C, Chowdury A, Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging. Nat Med. 2023;29(4):828\u2013832."},{"issue":"1","key":"b2","series-title":"Nat Med","first-page":"52","article-title":"Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks","volume":"26","author":"Hollon TC","year":"2020","unstructured":"Hollon TC, Pandian B, Adapa AR, Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat Med. 2020;26(1):52\u201358."},{"issue":"6","key":"b3","series-title":"Neurosurgery","first-page":"758","article-title":"Rapid automated analysis of skull base tumor specimens using intraoperative optical imaging and artificial intelligence","volume":"90","author":"Jiang C","year":"2022","unstructured":"Jiang C, Bhattacharya A, Linzey JR, Rapid automated analysis of skull base tumor specimens using intraoperative optical imaging and artificial intelligence. 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Nat Med. 2018;24(9):1337\u20131341."},{"issue":"13","key":"b6","series-title":"Spine (Phila Pa 1976)","first-page":"915","article-title":"Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value","volume":"44","author":"Ames CP","year":"2019","unstructured":"Ames CP, Smith JS, Pellis\u00e9 F, Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value. Spine (Phila Pa 1976). 2019;44(13):915\u2013926."},{"issue":"4","key":"b7","series-title":"Neurospine","first-page":"686","article-title":"Artificial intelligence for adult spinal deformity","volume":"16","author":"Joshi RS","year":"2019","unstructured":"Joshi RS, Haddad AF, Lau D, Ames CP. Artificial intelligence for adult spinal deformity. 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