{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:05:42Z","timestamp":1789517142601,"version":"build-2803163510"},"reference-count":30,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,11]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>This study aimed to develop and validate a fully automated vertebral bone quality (VBQ) pipeline for lumbar MRI studies and to assess its diagnostic performance with dual-energy x-ray absorptiometry (DXA)\u2013defined osteoporosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>Lumbar MRI studies from patients being evaluated for low-back pain were collected, deidentified, and assigned to reviewers for manual annotations. Each MRI study was manually annotated by two independent reviewers and by a custom, automated artificial intelligence (AI)\u2013based segmentation algorithm. The measured signal intensity from the regions of interest (ROIs) was then extracted. Agreement between manual and automated methods was assessed using intraclass correlation coefficients (ICCs), root mean square error, and the paired t-test. The automated algorithm was validated on a separate cohort of 100 MRI examinations with paired DXA studies. Diagnostic performance for identifying osteoporosis was evaluated using receiver operating characteristic analysis to calculate the area under the curve (AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>A total of 1089 patients were included to compare manual and automated VBQ calculations. Correlation between reviewer and automation was strong (r = 0.87, p &lt; 0.001), and agreement was good with an ICC of 0.86 (95% CI 0.81\u20130.89). Diagnostic capacity was validated in a separate cohort (n = 100) with an AUC of 0.79 (95% CI 0.63\u20130.92). The Youden-optimized VBQ score of 2.59 increased sensitivity from 0.20\u20130.50 to 0.66\u20130.80 while maintaining high specificity at 0.83\u20130.96.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>Automated VBQ measurement closely aligns with manual methods. An optimized threshold of 2.59 improved sensitivity for DXA-defined osteoporosis while maintaining high specificity. The automated algorithm may serve as an adjunct screening tool for osteoporosis in clinical practice and as a scalable research application suitable for integration into clinical workflows and large-scale MRI dataset analysis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2026.3.spine251469","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:29Z","timestamp":1789516829000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of an AI framework for automated vertebral bone quality assessment of lumbar MRI"],"prefix":"10.3171","author":[{"given":"Christian","family":"Quinones","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wesley","family":"Jameson","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Schwartz","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bailey","family":"Lupo","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joe","family":"Wolters","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Smith","family":"Surendran","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huy","family":"Tran","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak","family":"Kumbhare","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bharat","family":"Guthikonda","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanley","family":"Hoang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Louisiana State University Health Sciences Center, Shreveport, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"issue":"2","key":"b1","series-title":"Global Spine J","first-page":"554","article-title":"An automatized deep segmentation and classification model for lumbar disk degeneration and clarification of its impact on clinical decisions","volume":"15","author":"Soydan Z","year":"2025","unstructured":"Soydan Z, Bayramoglu E, Karasu R, Sayin I, Salturk S, Uvet H. An automatized deep segmentation and classification model for lumbar disk degeneration and clarification of its impact on clinical decisions. Global Spine J. 2025;15(2):554\u2013563. doi:10.1177\/21925682231200783"},{"issue":"1","key":"b2","series-title":"J Imaging Inform Med","first-page":"229","article-title":"Automated quantitative analysis of the lumbar spine: a comprehensive approach","volume":"39","author":"Kumar P","year":"2026","unstructured":"Kumar P, Singh S, Balabantaray BK, Nayak R. Automated quantitative analysis of the lumbar spine: a comprehensive approach. J Imaging Inform Med. 2026;39(1):229\u2013241. doi:10.1007\/s10278-025-01517-3"},{"key":"b3","series-title":"Comput Methods Programs Biomed Update","first-page":"100081","article-title":"A deep learning pipeline for automatized assessment of spinal MRI","volume":"2","author":"Balzer I","year":"2022","unstructured":"Balzer I, M\u00fchlemann M, Jokeit M,  A deep learning pipeline for automatized assessment of spinal MRI. Comput Methods Programs Biomed Update. 2022;2:100081. doi:10.1016\/j.cmpbup.2022.100081"},{"issue":"1","key":"b4","series-title":"Sci Rep","first-page":"593","article-title":"Automatic detection, classification, and segmentation of sagittal MR images for diagnosing prolapsed lumbar intervertebral disc","volume":"15","author":"Sayed MA","year":"2025","unstructured":"Sayed MA, Rahman GMM, Islam MS,  Automatic detection, classification, and segmentation of sagittal MR images for diagnosing prolapsed lumbar intervertebral disc. Sci Rep. 2025;15(1):593. doi:10.1038\/s41598-024-84301-7"},{"issue":"1","key":"b5","series-title":"J Neurosurg Spine","first-page":"83","article-title":"Utility of magnetic resonance imaging-based vertebral bone quality score in recognizing patients at risk for secondary fracture after vertebroplasty","volume":"43","author":"Kadri A","year":"2025","unstructured":"Kadri A, Liu D, Sorensen A, Binkley N, Ross A, Anderson PA. Utility of magnetic resonance imaging-based vertebral bone quality score in recognizing patients at risk for secondary fracture after vertebroplasty. J Neurosurg Spine. 2025;43(1):83\u201389. doi:10.3171\/2025.1.SPINE24976"},{"issue":"5","key":"b6","series-title":"Quant Imaging Med Surg","first-page":"4122","article-title":"Prediction of fusion cage subsidence after transforaminal lumbar interbody fusion based on MRI endplate bone quality","volume":"15","author":"Li C","year":"2025","unstructured":"Li C, Li D, Yao X,  Prediction of fusion cage subsidence after transforaminal lumbar interbody fusion based on MRI endplate bone quality. Quant Imaging Med Surg. 2025;15(5):4122\u20134134. doi:10.21037\/qims-20204-2533"},{"issue":"1","key":"b7","series-title":"Global Spine J","first-page":"162","article-title":"T2-weighted MRI-based vertebral bone quality score is an independent risk factor of osteoporotic vertebral compression fracture: an age- and sex-matched study","volume":"16","author":"Huang X","year":"2026","unstructured":"Huang X, Cai C, Huang S,  T2-weighted MRI-based vertebral bone quality score is an independent risk factor of osteoporotic vertebral compression fracture: an age- and sex-matched study. Global Spine J. 2026;16(1):162\u2013170. doi:10.1177\/21925682251339995"},{"issue":"4","key":"b8","series-title":"J Neurosurg Spine","first-page":"499","article-title":"A novel MRI-based score assessing trabecular bone quality to predict vertebral compression fractures in patients with spinal metastasis","volume":"32","author":"Ehresman J","year":"2020","unstructured":"Ehresman J, Schilling A, Pennington Z,  A novel MRI-based score assessing trabecular bone quality to predict vertebral compression fractures in patients with spinal metastasis. J Neurosurg Spine. 2020;32(4):499\u2013506. doi:10.3171\/2019.9.SPINE19954"},{"key":"b9","article-title":"Fully automatic vertebrae and spinal cord segmentation using a hybrid approach combining nnU-Net and iterative algorithm","author":"Warszawer Y","unstructured":"Warszawer Y, Molinier N, Valo\u0161ek J,  Fully automatic vertebrae and spinal cord segmentation using a hybrid approach combining nnU-Net and iterative algorithm. Abstract presented at: ISMRM & ISMRT Annual Meeting & Exhibition; May 4\u20139, 2024; Singapore. Accessed July 23,2026. https:\/\/archive.ismrm.org\/2024\/4847.html"},{"issue":"2","key":"b10","series-title":"Nat Methods","first-page":"203","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee F","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203\u2013211. doi:10.1038\/s41592-020-01008-z"},{"issue":"3","key":"b11","series-title":"Magn Reson Med Sci","first-page":"307","article-title":"Reproducible spinal cord quantitative MRI analysis with the Spinal Cord Toolbox","volume":"23","author":"Valo\u0161ek J","year":"2024","unstructured":"Valo\u0161ek J, Cohen-Adad J. Reproducible spinal cord quantitative MRI analysis with the Spinal Cord Toolbox. Magn Reson Med Sci. 2024;23(3):307\u2013315. doi:10.2463\/mrms.rev.2023-0159"},{"key":"b12","series-title":"World Health Organ Tech Rep Ser","first-page":"1","article-title":"Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO study group","volume":"843","year":"1994","unstructured":"Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: report of a WHO study group. World Health Organ Tech Rep Ser. 1994;843:1\u2013129.Accessed July 23, 2026. https:\/\/iris.who.int\/handle\/10665\/39142"},{"issue":"9","key":"b13","series-title":"Eur Spine J","first-page":"4035","article-title":"Assessing the impact of metabolic syndrome on the accuracy of vertebral bone quality in determining bone mineral density in patients undergoing lumbar fusion surgery","volume":"34","author":"Zhao X","year":"2025","unstructured":"Zhao X, Wang W, Chen X, Lu S. Assessing the impact of metabolic syndrome on the accuracy of vertebral bone quality in determining bone mineral density in patients undergoing lumbar fusion surgery. Eur Spine J. 2025;34(9):4035\u20134042. doi:10.1007\/s00586-025-08934-6"},{"issue":"8","key":"b14","series-title":"Spine J","first-page":"1172","article-title":"Assessment of bone density using the 1.5 T or 3.0 T MRI-based vertebral bone quality score in older patients undergoing spine surgery: does field strength matter?","volume":"23","author":"Lin W","year":"2023","unstructured":"Lin W, He C, Xie F,  Assessment of bone density using the 1.5 T or 3.0 T MRI-based vertebral bone quality score in older patients undergoing spine surgery: does field strength matter?. Spine J. 2023;23(8):1172\u20131181. doi:10.1016\/j.spinee.2023.03.016"},{"key":"b15","series-title":"Bone Rep","first-page":"101711","article-title":"A meta-analysis of the value of MRI-based VBQ scores for evaluating osteoporosis","volume":"19","author":"Chen A","year":"2023","unstructured":"Chen A, Feng S, Lai L, Yan C. A meta-analysis of the value of MRI-based VBQ scores for evaluating osteoporosis. Bone Rep. 2023;19:101711. doi:10.1016\/j.bonr.2023.101711"},{"issue":"1","key":"b16","series-title":"Spine J","first-page":"20","article-title":"Vertebral bone quality score predicts fragility fractures independently of bone mineral density","volume":"21","author":"Ehresman J","year":"2021","unstructured":"Ehresman J, Schilling A, Yang X,  Vertebral bone quality score predicts fragility fractures independently of bone mineral density. Spine J. 2021;21(1):20\u201327. doi:10.1016\/j.spinee.2020.05.540"},{"issue":"6","key":"b17","series-title":"J Neurosurg Spine","first-page":"705","article-title":"Vertebral bone quality score provides preoperative bone density assessment for patients undergoing lumbar spine surgery: a retrospective study","volume":"38","author":"Pu M","year":"2023","unstructured":"Pu M, Zhong W, Heng H,  Vertebral bone quality score provides preoperative bone density assessment for patients undergoing lumbar spine surgery: a retrospective study. J Neurosurg Spine. 2023;38(6):705\u2013714. doi:10.3171\/2023.1.SPINE221187"},{"key":"b18","series-title":"spine-generic\/data-multi-subject: r20250314","author":"Cohen-Adad J","unstructured":"Cohen-Adad J, Guenther N, Foias A,  spine-generic\/data-multi-subject: r20250314. Published online March 14, 2025. doi:10.5281\/zenodo.15028452"},{"key":"b19","series-title":"Mendeley Data","article-title":"Lumbar spine MRI dataset","volume":"2","author":"Sudirman S","year":"2019","unstructured":"Sudirman S, Al Kafri A, Natalia F,  Lumbar spine MRI dataset. Mendeley Data. 2019;2.Accessed July 23, 2026. https:\/\/data.mendeley.com\/datasets\/k57fr854j2\/2"},{"issue":"1","key":"b20","series-title":"Sci Data","first-page":"97","article-title":"Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database","volume":"9","author":"Khalil YA","year":"2022","unstructured":"Khalil YA, Becherucci EA, Kirschke JS,  Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database. Sci Data. 2022;9(1):97. doi:10.1038\/s41597-022-01222-8"},{"key":"b21","series-title":"Zenodo","article-title":"SPIDER - Lumbar spine segmentation in MR images: a dataset and a public benchmark","author":"van der Graaf JW","unstructured":"van der Graaf JW, van Hooff ML, Buckens CFM,  SPIDER - Lumbar spine segmentation in MR images: a dataset and a public benchmark. Zenodo. Published online November 20, 2023. Accessed August 31, 2025. https:\/\/zenodo.org\/records\/10159290"},{"issue":"1","key":"b22","series-title":"Sci Data","first-page":"1131","article-title":"An open-access lumbosacral spine MRI dataset with enhanced spinal nerve root structure resolution","volume":"11","author":"Liu J","year":"2024","unstructured":"Liu J, Zhang W, Zhou Y, Xu L, Chu YH, Jia F. An open-access lumbosacral spine MRI dataset with enhanced spinal nerve root structure resolution. Sci Data. 2024;11(1):1131. doi:10.1038\/s41597-024-03919-4"},{"issue":"1","key":"b23","series-title":"Sci Data","first-page":"264","article-title":"Lumbar spine segmentation in MR images: a dataset and a public benchmark","volume":"11","author":"van der Graaf JW","year":"2024","unstructured":"van der Graaf JW, van Hooff ML, Buckens CFM,  Lumbar spine segmentation in MR images: a dataset and a public benchmark. Sci Data. 2024;11(1):264. doi:10.1038\/s41597-024-03090-w"},{"key":"b24","series-title":"arXiv","article-title":"The RSNA lumbar degenerative imaging spine classification (LumbarDISC) dataset","author":"Richards TJ","unstructured":"Richards TJ, Flanders AE, Colak E,  The RSNA lumbar degenerative imaging spine classification (LumbarDISC) dataset. arXiv. Preprint posted online June 10, 2025. doi:10.48550\/arXiv.2506.09162"},{"issue":"8","key":"b25","series-title":"Spine J","first-page":"1301","article-title":"Preoperative MRI-based vertebral bone quality (VBQ) score assessment in patients undergoing lumbar spinal fusion","volume":"22","author":"Salzmann SN","year":"2022","unstructured":"Salzmann SN, Okano I, Jones C,  Preoperative MRI-based vertebral bone quality (VBQ) score assessment in patients undergoing lumbar spinal fusion. Spine J. 2022;22(8):1301\u20131308. doi:10.1016\/j.spinee.2022.03.006"},{"issue":"12","key":"b26","series-title":"Eur Radiol","first-page":"8637","article-title":"Comparison of predictive performance for cage subsidence between CT-based Hounsfield units and MRI-based vertebral bone quality score following oblique lumbar interbody fusion","volume":"33","author":"Pu X","year":"2023","unstructured":"Pu X, Wang X, Ran L,  Comparison of predictive performance for cage subsidence between CT-based Hounsfield units and MRI-based vertebral bone quality score following oblique lumbar interbody fusion. Eur Radiol. 2023;33(12):8637\u20138644. doi:10.1007\/s00330-023-09929-x"},{"issue":"4","key":"b27","series-title":"Cureus","first-page":"e82154","article-title":"Unlocking osteoporosis diagnosis: unveiling the potential of MRI-based vertebral bone quality score","volume":"17","author":"Pabbisetti D","year":"2025","unstructured":"Pabbisetti D, Gudipati A, Papalkar A,  Unlocking osteoporosis diagnosis: unveiling the potential of MRI-based vertebral bone quality score. Cureus. 2025;17(4):e82154. doi:10.7759\/cureus.82154"},{"issue":"2","key":"b28","series-title":"Eur Spine J","first-page":"506","article-title":"A novel T2-weighted series-based modified vertebral bone quality score for evaluating bone mineral density","volume":"34","author":"Yan S","year":"2025","unstructured":"Yan S, Liu Y, Yuan L, Du G, Li W, Zeng Y. A novel T2-weighted series-based modified vertebral bone quality score for evaluating bone mineral density. Eur Spine J. 2025;34(2):506\u2013512. doi:10.1007\/s00586-024-08522-0"},{"issue":"3","key":"b29","series-title":"J Clin Densitom","first-page":"344","article-title":"National Health and Nutrition Examination Survey whole-body dual-energy x-ray absorptiometry reference data for GE Lunar systems","volume":"17","author":"Fan B","year":"2014","unstructured":"Fan B, Shepherd JA, Levine MA,  National Health and Nutrition Examination Survey whole-body dual-energy x-ray absorptiometry reference data for GE Lunar systems. J Clin Densitom. 2014;17(3):344\u2013377. doi:10.1016\/j.jocd.2013.08.019"},{"key":"b30","series-title":"Clin Neurol Neurosurg","first-page":"109094","article-title":"Automated vertebral bone quality score measurement on lumbar MRI using deep learning: development and validation of an AI algorithm","volume":"257","author":"Jayasuriya NM","year":"2025","unstructured":"Jayasuriya NM, Feng E, Nathani KR,  Automated vertebral bone quality score measurement on lumbar MRI using deep learning: development and validation of an AI algorithm. 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