{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T15:00:02Z","timestamp":1781190002161,"version":"3.54.1"},"reference-count":90,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,27]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>Lumbar spinal stenosis (LSS) is a degenerative spinal condition characterized by the narrowing of the lumbar spinal canal, leading to back pain and disability. MRI remains the gold standard for LSS diagnosis, but diagnostic variability arises due to the lack of standardized imaging criteria. Recent advancements in artificial intelligence, particularly convolutional neural networks (CNNs), offer promising potential for automating LSS detection and classification. The aim of this study was to propose a novel 3-stage deep learning pipeline for automated LSS identification, classification, and grading using lumbar MRI, aiming to enhance diagnostic accuracy and consistency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS<\/jats:title>\n                    <jats:p>Two datasets were used. The first dataset consisted of 17,440 MRI slices obtained in 640 patients (mean patient age 57.58 \u00b1 12.47 years) and was used for model training. The second dataset consisted of 8000 slices and was used only as the external validation set. The proposed framework consists of 1) classification of images into sacral, lumbar, and thoracic regions; 2) region of interest detection; and 3) LSS grading (binary and multiclass). The 10-fold cross-validation method was used to avoid overfitting and improve generalization of the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>The proposed model achieved an accuracy of 97.87% for binary classification and 95.52% for multiclass grading of LSS, outperforming state-of-the-art models. To validate the clinical relevance of the model\u2019s decision-making, gradient-weighted class activation mapping was used to visualize the key focus areas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>The proposed framework offers a reliable, interpretable, and effective tool for automated LSS detection and grading, with the potential for future improvements in underdiagnosis and multilevel spine disease analysis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3171\/2025.10.spine25878","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T10:04:08Z","timestamp":1775642648000},"page":"847-857","source":"Crossref","is-referenced-by-count":0,"title":["A novel interpretable classification of lumbar spinal stenosis using a cascade deep learning approach and T2-weighted MRI"],"prefix":"10.3171","volume":"44","author":[{"given":"Mohammad","family":"Tabarestani","sequence":"first","affiliation":[{"name":"Neuraitex Research Center, School of Electrical and Computer Engineering, College of Engineering, University of Tehran;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niloufar","family":"Delfan","sequence":"additional","affiliation":[{"name":"Neuraitex Research Center, School of Electrical and Computer Engineering, College of Engineering, University of Tehran;"},{"name":"School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Khoshnevisan","sequence":"additional","affiliation":[{"name":"Physics Department, College of Science, Northeastern University, Boston, Massachusetts;"},{"name":"Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Antalya Bilim University, Antalya, Turkey;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaber","family":"Hatam Parikhan","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences, Tehran;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amirmohammad","family":"Bahri","sequence":"additional","affiliation":[{"name":"Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; and"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amin","family":"Hessam","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences, Tehran;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohsen","family":"Nabiuni","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, School of Medicine, Iran University of Medical Sciences, Tehran;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Behzad","family":"Moshiri","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Iran;"},{"name":"Department of Electrical and Computer Engineering, University of Waterloo, Ontario, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"key":"b1","series-title":"World Neurosurg X","first-page":"100073","article-title":"Natural course and diagnosis of lumbar spinal stenosis: WFNS Spine Committee Recommendations","volume":"7","author":"Zileli M","year":"2020","unstructured":"Zileli M, Crostelli M, Grimaldi M,  Natural course and diagnosis of lumbar spinal stenosis: WFNS Spine Committee Recommendations. World Neurosurg X. 2020;7:100073. doi:10.1016\/j.wnsx.2020.100073"},{"key":"b2","series-title":"AME Med J","first-page":"63","article-title":"Lumbar spinal stenosis: an update on the epidemiology, diagnosis and treatment","volume":"2","author":"Wu AM","year":"2017","unstructured":"Wu AM, Zou F, Cao Y,  Lumbar spinal stenosis: an update on the epidemiology, diagnosis and treatment. AME Med J. 2017;2:63."},{"issue":"13","key":"b3","series-title":"JAMA","first-page":"1259","article-title":"Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults","volume":"303","author":"Deyo RA","year":"2010","unstructured":"Deyo RA, Mirza SK, Martin BI, Kreuter W, Goodman DC, Jarvik JG. Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. JAMA. 2010;303(13):1259\u20131265. doi:10.1001\/jama.2010.338"},{"issue":"8","key":"b4","series-title":"N Engl J Med","first-page":"818","article-title":"Lumbar spinal stenosis","volume":"358","author":"Katz JN","year":"2008","unstructured":"Katz JN, Harris MB. Lumbar spinal stenosis. N Engl J Med. 2008;358(8):818\u2013825. doi:10.1056\/NEJMcp0708097"},{"key":"b5","series-title":"BMJ","first-page":"h6234","article-title":"Management of lumbar spinal stenosis","volume":"352","author":"Lurie J","year":"2016","unstructured":"Lurie J, Tomkins-Lane C. Management of lumbar spinal stenosis. BMJ. 2016;352:h6234. doi:10.1136\/bmj.h6234"},{"issue":"4","key":"b6","series-title":"Rheumatol Int","first-page":"619","article-title":"Real-world incidence and prevalence of low back pain using routinely collected data","volume":"39","author":"Fatoye F","year":"2019","unstructured":"Fatoye F, Gebrye T, Odeyemi I. Real-world incidence and prevalence of low back pain using routinely collected data. Rheumatol Int. 2019;39(4):619\u2013626. doi:10.1007\/s00296-019-04273-0"},{"key":"b7","series-title":"Clin Orthop Relat Res","first-page":"198","article-title":"Spinal stenosis: surgical versus nonsurgical treatment","volume":"443","author":"Atlas SJ","year":"2006","unstructured":"Atlas SJ, Delitto A. Spinal stenosis: surgical versus nonsurgical treatment. Clin Orthop Relat Res. 2006;443:198\u2013207. doi:10.1097\/01.blo.0000198722.70138.96"},{"issue":"7","key":"b8","series-title":"Spine (Phila Pa 1976)","first-page":"803","article-title":"Variation in eligibility criteria from studies of radiculopathy due to a herniated disc and of neurogenic claudication due to lumbar spinal stenosis: a structured literature review","volume":"35","author":"Genevay S","year":"2010","unstructured":"Genevay S, Atlas SJ, Katz JN. Variation in eligibility criteria from studies of radiculopathy due to a herniated disc and of neurogenic claudication due to lumbar spinal stenosis: a structured literature review. Spine (Phila Pa 1976). 2010;35(7):803\u2013811. doi:10.1097\/BRS.0b013e3181bc9454"},{"issue":"10","key":"b9","series-title":"Spine J","first-page":"2442","article-title":"Interrater and intrarater agreements of magnetic resonance imaging findings in the lumbar spine: significant variability across degenerative conditions","volume":"14","author":"Fu MC","year":"2014","unstructured":"Fu MC, Buerba RA, Long WD,  Interrater and intrarater agreements of magnetic resonance imaging findings in the lumbar spine: significant variability across degenerative conditions. Spine J. 2014;14(10):2442\u20132448. doi:10.1016\/j.spinee.2014.03.010"},{"key":"b10","series-title":"Clin Imaging","first-page":"83","article-title":"Quantitative magnetic resonance imaging (q-MRI) for the assessment of soft-tissue sarcoma treatment response: a narrative case review of technique development","volume":"63","author":"Fields BKK","year":"2020","unstructured":"Fields BKK, Hwang D, Cen S,  Quantitative magnetic resonance imaging (q-MRI) for the assessment of soft-tissue sarcoma treatment response: a narrative case review of technique development. Clin Imaging. 2020;63:83\u201393. doi:10.1016\/j.clinimag.2020.02.016"},{"issue":"4","key":"b11","series-title":"Acta Inform Med","first-page":"229","article-title":"Relationship between magnetic resonance imaging findings and clinical symptoms in patients with suspected lumbar spinal canal stenosis: a case-control study","volume":"27","author":"Majidi H","year":"2019","unstructured":"Majidi H, Shafizad M, Niksolat F,  Relationship between magnetic resonance imaging findings and clinical symptoms in patients with suspected lumbar spinal canal stenosis: a case-control study. Acta Inform Med. 2019;27(4):229\u2013233. doi:10.5455\/aim.2019.27.229-233"},{"key":"b12","series-title":"BMC Musculoskelet Disord","first-page":"175","article-title":"Quantitative radiologic criteria for the diagnosis of lumbar spinal stenosis: a systematic literature review","volume":"12","author":"Steurer J","year":"2011","unstructured":"Steurer J, Roner S, Gnannt R, Hodler J. Quantitative radiologic criteria for the diagnosis of lumbar spinal stenosis: a systematic literature review. BMC Musculoskelet Disord. 2011;12:175\u2013179. doi:10.1186\/1471-2474-12-175"},{"key":"b13","series-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"893","article-title":"Fully automatic segmentation of lumbar vertebrae from CT images using cascaded 3D fully convolutional networks","author":"Janssens R","year":"2018","unstructured":"Janssens R, Zeng G, Zheng G. Fully automatic segmentation of lumbar vertebrae from CT images using cascaded 3D fully convolutional networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE; 2018:893\u2013897. doi:10.1109\/ISBI.2018.8363715"},{"issue":"1","key":"b14","series-title":"Sci Rep","first-page":"22156","article-title":"Automatic vertebrae localization and segmentation in CT with a two-stage Dense-U-Net","volume":"11","author":"Cheng P","year":"2021","unstructured":"Cheng P, Yang Y, Yu H, He Y. Automatic vertebrae localization and segmentation in CT with a two-stage Dense-U-Net. Sci Rep. 2021;11(1):22156. doi:10.1038\/s41598-021-01296-1"},{"key":"b15","series-title":"VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","first-page":"124","article-title":"Coarse to fine vertebrae localization and segmentation with SpatialConfiguration-Net and U-Net","volume":"5","author":"Payer C","year":"2020","unstructured":"Payer C, Stern D, Bischof H, Urschler M. Coarse to fine vertebrae localization and segmentation with SpatialConfiguration-Net and U-Net. In: VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Vol 5. SciTePress;2020:124\u2013133. doi:10.5220\/0008975201240133"},{"issue":"16","key":"b16","series-title":"Diagnostics (Basel)","first-page":"2658","article-title":"An automated deep learning approach for spine segmentation and vertebrae recognition using computed tomography images","volume":"13","author":"Saeed MU","year":"2023","unstructured":"Saeed MU, Dikaios N, Dastgir A, Ali G, Hamid M, Hajjej F. An automated deep learning approach for spine segmentation and vertebrae recognition using computed tomography images. Diagnostics (Basel). 2023;13(16):2658. doi:10.3390\/diagnostics13162658"},{"key":"b17","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science","article-title":"Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks","volume":"9349","author":"Chen H","year":"2015","unstructured":"Chen H, Shen C, Qin J,  Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks. In: Navab N, Hornegger J, Wells W, Frangi A, eds. Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science. Vol 9349. Springer;2015. doi:10.1007\/978-3-319-24553-9_63"},{"key":"b18","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science","article-title":"Fast automatic vertebrae detection and localization in pathological CT scans\u2014a deep learning approach","volume":"9351","author":"Suzani A","year":"2015","unstructured":"Suzani A, Seitel A, Liu Y, Fels S, Rohling RN, Abolmaesumi P. Fast automatic vertebrae detection and localization in pathological CT scans\u2014a deep learning approach. In: Navab N, Hornegger J, Wells W, Frangi A, eds. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science. Vol 9351.Springer;2015. doi:10.1007\/978-3-319-24574-4_81"},{"key":"b19","series-title":"Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science","article-title":"Shape-aware deep convolutional neural network for vertebrae segmentation","volume":"10734","author":"Al Arif S","year":"2018","unstructured":"Al Arif S, Knapp K, Slabaugh G. Shape-aware deep convolutional neural network for vertebrae segmentation. In: Glocker B, Yao J, Vrtovec T, Frangi A, Zheng G, eds. Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science. Vol 10734. Springer;2018. doi:10.1007\/978-3-319-74113-0_2"},{"key":"b20","series-title":"Comput Methods Programs Biomed","first-page":"105833","article-title":"Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation","volume":"200","author":"Kim KC","year":"2021","unstructured":"Kim KC, Cho HC, Jang TJ, Choi JM, Seo JK. Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation. Comput Methods Programs Biomed. 2021;200:105833. doi:10.1016\/j.cmpb.2020.105833"},{"key":"b21","series-title":"Comput Biol Med","first-page":"105105","article-title":"Fully automated segmentation of lumbar bone marrow in sagittal, high-resolution T1-weighted magnetic resonance images using 2D U-NET","volume":"140","author":"Hwang EJ","year":"2022","unstructured":"Hwang EJ, Kim S, Jung JY. Fully automated segmentation of lumbar bone marrow in sagittal, high-resolution T1-weighted magnetic resonance images using 2D U-NET. Comput Biol Med. 2022;140:105105. doi:10.1016\/j.compbiomed.2021.105105"},{"issue":"3","key":"b22","series-title":"J Radiat Res Appl Sci","first-page":"100627","article-title":"Spinal magnetic resonance image segmentation based on U-net","volume":"16","author":"Wang Z","year":"2023","unstructured":"Wang Z, Xiao P, Tan H. Spinal magnetic resonance image segmentation based on U-net. J Radiat Res Appl Sci. 2023;16(3):100627. doi:10.1016\/j.jrras.2023.100627"},{"issue":"3","key":"b23","series-title":"J Digit Imaging","first-page":"513","article-title":"Automatic lumbar MRI detection and identification based on deep learning","volume":"32","author":"Zhou Y","year":"2019","unstructured":"Zhou Y, Liu Y, Chen Q, Gu G, Sui X. Automatic lumbar MRI detection and identification based on deep learning. J Digit Imaging. 2019;32(3):513\u2013520. doi:10.1007\/s10278-018-0130-7"},{"issue":"4","key":"b24","series-title":"J Digit Imaging","first-page":"406","article-title":"Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data","volume":"30","author":"Forsberg D","year":"2017","unstructured":"Forsberg D, Sj\u00f6blom E, Sunshine JL. Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data. J Digit Imaging. 2017;30(4):406\u2013412. doi:10.1007\/s10278-017-9945-x"},{"key":"b25","series-title":"Proc Mach Learn Res","first-page":"403","article-title":"Deep Spine: Automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning","volume":"85","author":"Lu JT","year":"2018","unstructured":"Lu JT, Pedemonte S, Bizzo B,  Deep Spine: Automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning. Proc Mach Learn Res. 2018;85:403\u2013419."},{"issue":"1","key":"b26","series-title":"Radiology","first-page":"130","article-title":"Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI","volume":"300","author":"Hallinan JTPD","year":"2021","unstructured":"Hallinan JTPD, Zhu L, Yang K,  Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI. Radiology. 2021;300(1):130\u2013138. doi:10.1148\/radiol.2021204289"},{"issue":"3","key":"b27","series-title":"Eur Spine J","first-page":"941","article-title":"A neural network model for detection and classification of lumbar spinal stenosis on MRI","volume":"33","author":"Tumko V","year":"2024","unstructured":"Tumko V, Kim J, Uspenskaia N,  A neural network model for detection and classification of lumbar spinal stenosis on MRI. Eur Spine J. 2024;33(3):941\u2013948. doi:10.1007\/s00586-023-08089-2"},{"issue":"5","key":"b28","series-title":"Clin Spine Surg","first-page":"E180","article-title":"LSS-VGG16: diagnosis of lumbar spinal stenosis with deep learning","volume":"36","author":"Altun S","year":"2023","unstructured":"Altun S, Alkan A, Altun \u0130. LSS-VGG16: diagnosis of lumbar spinal stenosis with deep learning. Clin Spine Surg. 2023;36(5):E180\u2013E190. doi:10.1097\/BSD.0000000000001418"},{"issue":"5","key":"b29","series-title":"Eur Radiol","first-page":"3435","article-title":"Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI","volume":"33","author":"Bharadwaj UU","year":"2023","unstructured":"Bharadwaj UU, Christine M, Li S,  Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI. Eur Radiol. 2023;33(5):3435\u20133443. doi:10.1007\/s00330-023-09483-6"},{"issue":"2","key":"b30","series-title":"Asian Spine J","first-page":"207","article-title":"Diffusion tensor imaging of the spinal canal in quantitative assessment of patients with lumbar spinal canal stenosis","volume":"15","author":"Norimoto M","year":"2021","unstructured":"Norimoto M, Eguchi Y, Kanamoto H,  Diffusion tensor imaging of the spinal canal in quantitative assessment of patients with lumbar spinal canal stenosis. Asian Spine J. 2021;15(2):207\u2013215. doi:10.31616\/asj.2020.0026"},{"issue":"5","key":"b31","series-title":"PLoS One","first-page":"e0233633","article-title":"Clinical validity of two different grading systems for lumbar central canal stenosis: Schizas and Lee classification systems","volume":"15","author":"Ko YJ","year":"2020","unstructured":"Ko YJ, Lee E, Lee JW,  Clinical validity of two different grading systems for lumbar central canal stenosis: Schizas and Lee classification systems. PLoS One. 2020;15(5):e0233633. doi:10.1371\/journal.pone.0233633"},{"key":"b32","series-title":"GitHub","article-title":"Label Studio: Data labeling software","author":"Tkachenko M","unstructured":"Tkachenko M, Malyuk M, Holmanyuk A, Liubimov N. Label Studio: Data labeling software. GitHub. Accessed January 8, 2026. https:\/\/github.com\/heartexlabs\/label-studio"},{"key":"b33","series-title":"2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS)","first-page":"1449","article-title":"Development of ground truth data for automatic lumbar spine MRI image segmentation","author":"Natalia F","year":"2018","unstructured":"Natalia F, Meidia H, Afriliana N,  Development of ground truth data for automatic lumbar spine MRI image segmentation. In: 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS). IEEE; 2018:1449\u20131454. doi:10.1109\/HPCC\/SmartCity\/DSS.2018.00239"},{"key":"b34","series-title":"IEEE Access","first-page":"43487","article-title":"Boundary delineation of MRI images for lumbar spinal stenosis detection through semantic segmentation using deep neural networks","volume":"7","author":"Al-Kafri AS","year":"2019","unstructured":"Al-Kafri AS, Sudirman S, Hussain A,  Boundary delineation of MRI images for lumbar spinal stenosis detection through semantic segmentation using deep neural networks. IEEE Access. 2019;7:43487\u201343501. doi:10.1109\/ACCESS.2019.2908002"},{"key":"b35","series-title":"arXiv","article-title":"Battle of the backbones: a large-scale comparison of pretrained models across computer vision tasks","author":"Goldblum M","unstructured":"Goldblum M, Souri H, Ni R,  Battle of the backbones: a large-scale comparison of pretrained models across computer vision tasks. arXiv. Preprint published online November 20, 2023. doi:10.48550\/arXiv.2310.19909"},{"key":"b36","series-title":"2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)","first-page":"114","article-title":"A new approach to automatic disc localization in clinical lumbar MRI: combining machine learning with heuristics","author":"Ghosh S","year":"2012","unstructured":"Ghosh S, Malgireddy MR, Chaudhary V, Dhillon G. A new approach to automatic disc localization in clinical lumbar MRI: combining machine learning with heuristics. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE; 2012:114\u2013117. doi:10.1109\/ISBI.2012.6235497"},{"issue":"9","key":"b37","series-title":"IEEE Trans Biomed Eng","first-page":"2375","article-title":"Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM-based MRF","volume":"60","author":"Oktay AB","year":"2013","unstructured":"Oktay AB, Akgul YS. Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM-based MRF. IEEE Trans Biomed Eng. 2013;60(9):2375\u20132383. doi:10.1109\/TBME.2013.2256460"},{"key":"b38","series-title":"Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics","first-page":"219","article-title":"Vertebrae detection and labelling in lumbar MR images","volume":"17","author":"Lootus M","year":"2014","unstructured":"Lootus M, Kadir T, Zisserman A. Vertebrae detection and labelling in lumbar MR images. In: Yao J, Klinder T, Li S, eds. Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics. Vol 17. Springer;2014:219\u2013230. doi:10.1007\/978-3-319-07269-2_19"},{"issue":"1","key":"b39","series-title":"IEEE Trans Med Imaging","first-page":"1","article-title":"Labeling of lumbar discs using both pixel-and object-level features with a two-level probabilistic model","volume":"30","author":"Alomari RS","year":"2010","unstructured":"Alomari RS, Corso JJ, Chaudhary V. Labeling of lumbar discs using both pixel-and object-level features with a two-level probabilistic model. IEEE Trans Med Imaging. 2010;30(1):1\u201310. doi:10.1109\/TMI.2010.2047403"},{"issue":"Pt 1","key":"b40","series-title":"Med Image Comput Comput Assist Interv","first-page":"202","article-title":"Lumbar disc localization and labeling with a probabilistic model on both pixel and object features","volume":"11","author":"Corso JJ","year":"2008","unstructured":"Corso JJ, Alomari RS, Chaudhary V. Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. Med Image Comput Comput Assist Interv. 2008;11(Pt 1):202-210. doi:10.1007\/978-3-540-85988-8_25"},{"issue":"4","key":"b41","series-title":"J Nucl Med","first-page":"488","article-title":"Introduction to radiomics","volume":"61","author":"Mayerhoefer ME","year":"2020","unstructured":"Mayerhoefer ME, Materka A, Langs G,  Introduction to radiomics. J Nucl Med. 2020;61(4):488\u2013495. doi:10.2967\/jnumed.118.222893"},{"issue":"12","key":"b42","series-title":"Spine (Phila Pa 1976)","first-page":"804","article-title":"Spinal stenosis grading in magnetic resonance imaging using deep convolutional neural networks","volume":"45","author":"Won D","year":"2020","unstructured":"Won D, Lee HJ, Lee SJ, Park SH. Spinal stenosis grading in magnetic resonance imaging using deep convolutional neural networks. Spine (Phila Pa 1976).2020;45(12):804\u2013812. doi:10.1097\/BRS.0000000000003377"},{"issue":"18","key":"b43","series-title":"Diagnostics (Basel)","first-page":"2975","article-title":"Nerve root compression analysis to find lumbar spine stenosis on MRI Using CNN","volume":"13","author":"Shahzadi T","year":"2023","unstructured":"Shahzadi T, Ali MU, Majeed F,  Nerve root compression analysis to find lumbar spine stenosis on MRI Using CNN. Diagnostics (Basel). 2023;13(18):2975. doi:10.3390\/diagnostics13182975"},{"key":"b44","series-title":"Med Image Anal","first-page":"63","article-title":"SpineNet: automated classification and evidence visualization in spinal MRIs","volume":"41","author":"Jamaludin A","year":"2017","unstructured":"Jamaludin A, Kadir T, Zisserman A. SpineNet: automated classification and evidence visualization in spinal MRIs. Med Image Anal. 2017;41:63\u201373. doi:10.1016\/j.media.2017.07.002"},{"issue":"1","key":"b45","series-title":"J Neurosurg Spine","first-page":"104","article-title":"Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs","volume":"37","author":"Kim T","year":"2022","unstructured":"Kim T, Kim YG, Park S,  Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs. J Neurosurg Spine. 2022;37(1):104\u2013111. doi:10.3171\/2021.11.SPINE211136"},{"key":"b1","series-title":"World Neurosurg X","first-page":"100073","article-title":"Natural course and diagnosis of lumbar spinal stenosis: WFNS Spine Committee Recommendations","volume":"7","author":"Zileli M","year":"2020","unstructured":"Zileli M, Crostelli M, Grimaldi M,  Natural course and diagnosis of lumbar spinal stenosis: WFNS Spine Committee Recommendations. World Neurosurg X. 2020;7:100073. doi:10.1016\/j.wnsx.2020.100073"},{"key":"b2","series-title":"AME Med J","first-page":"63","article-title":"Lumbar spinal stenosis: an update on the epidemiology, diagnosis and treatment","volume":"2","author":"Wu AM","year":"2017","unstructured":"Wu AM, Zou F, Cao Y,  Lumbar spinal stenosis: an update on the epidemiology, diagnosis and treatment. AME Med J. 2017;2:63."},{"issue":"13","key":"b3","series-title":"JAMA","first-page":"1259","article-title":"Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults","volume":"303","author":"Deyo RA","year":"2010","unstructured":"Deyo RA, Mirza SK, Martin BI, Kreuter W, Goodman DC, Jarvik JG. Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. JAMA. 2010;303(13):1259\u20131265. doi:10.1001\/jama.2010.338"},{"issue":"8","key":"b4","series-title":"N Engl J Med","first-page":"818","article-title":"Lumbar spinal stenosis","volume":"358","author":"Katz JN","year":"2008","unstructured":"Katz JN, Harris MB. Lumbar spinal stenosis. N Engl J Med. 2008;358(8):818\u2013825. doi:10.1056\/NEJMcp0708097"},{"key":"b5","series-title":"BMJ","first-page":"h6234","article-title":"Management of lumbar spinal stenosis","volume":"352","author":"Lurie J","year":"2016","unstructured":"Lurie J, Tomkins-Lane C. Management of lumbar spinal stenosis. BMJ. 2016;352:h6234. doi:10.1136\/bmj.h6234"},{"issue":"4","key":"b6","series-title":"Rheumatol Int","first-page":"619","article-title":"Real-world incidence and prevalence of low back pain using routinely collected data","volume":"39","author":"Fatoye F","year":"2019","unstructured":"Fatoye F, Gebrye T, Odeyemi I. Real-world incidence and prevalence of low back pain using routinely collected data. Rheumatol Int. 2019;39(4):619\u2013626. doi:10.1007\/s00296-019-04273-0"},{"key":"b7","series-title":"Clin Orthop Relat Res","first-page":"198","article-title":"Spinal stenosis: surgical versus nonsurgical treatment","volume":"443","author":"Atlas SJ","year":"2006","unstructured":"Atlas SJ, Delitto A. Spinal stenosis: surgical versus nonsurgical treatment. Clin Orthop Relat Res. 2006;443:198\u2013207. doi:10.1097\/01.blo.0000198722.70138.96"},{"issue":"7","key":"b8","series-title":"Spine (Phila Pa 1976)","first-page":"803","article-title":"Variation in eligibility criteria from studies of radiculopathy due to a herniated disc and of neurogenic claudication due to lumbar spinal stenosis: a structured literature review","volume":"35","author":"Genevay S","year":"2010","unstructured":"Genevay S, Atlas SJ, Katz JN. Variation in eligibility criteria from studies of radiculopathy due to a herniated disc and of neurogenic claudication due to lumbar spinal stenosis: a structured literature review. Spine (Phila Pa 1976). 2010;35(7):803\u2013811. doi:10.1097\/BRS.0b013e3181bc9454"},{"issue":"10","key":"b9","series-title":"Spine J","first-page":"2442","article-title":"Interrater and intrarater agreements of magnetic resonance imaging findings in the lumbar spine: significant variability across degenerative conditions","volume":"14","author":"Fu MC","year":"2014","unstructured":"Fu MC, Buerba RA, Long WD,  Interrater and intrarater agreements of magnetic resonance imaging findings in the lumbar spine: significant variability across degenerative conditions. Spine J. 2014;14(10):2442\u20132448. doi:10.1016\/j.spinee.2014.03.010"},{"key":"b10","series-title":"Clin Imaging","first-page":"83","article-title":"Quantitative magnetic resonance imaging (q-MRI) for the assessment of soft-tissue sarcoma treatment response: a narrative case review of technique development","volume":"63","author":"Fields BKK","year":"2020","unstructured":"Fields BKK, Hwang D, Cen S,  Quantitative magnetic resonance imaging (q-MRI) for the assessment of soft-tissue sarcoma treatment response: a narrative case review of technique development. Clin Imaging. 2020;63:83\u201393. doi:10.1016\/j.clinimag.2020.02.016"},{"issue":"4","key":"b11","series-title":"Acta Inform Med","first-page":"229","article-title":"Relationship between magnetic resonance imaging findings and clinical symptoms in patients with suspected lumbar spinal canal stenosis: a case-control study","volume":"27","author":"Majidi H","year":"2019","unstructured":"Majidi H, Shafizad M, Niksolat F,  Relationship between magnetic resonance imaging findings and clinical symptoms in patients with suspected lumbar spinal canal stenosis: a case-control study. Acta Inform Med. 2019;27(4):229\u2013233. doi:10.5455\/aim.2019.27.229-233"},{"key":"b12","series-title":"BMC Musculoskelet Disord","first-page":"175","article-title":"Quantitative radiologic criteria for the diagnosis of lumbar spinal stenosis: a systematic literature review","volume":"12","author":"Steurer J","year":"2011","unstructured":"Steurer J, Roner S, Gnannt R, Hodler J. Quantitative radiologic criteria for the diagnosis of lumbar spinal stenosis: a systematic literature review. BMC Musculoskelet Disord. 2011;12:175\u2013179. doi:10.1186\/1471-2474-12-175"},{"key":"b13","series-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"893","article-title":"Fully automatic segmentation of lumbar vertebrae from CT images using cascaded 3D fully convolutional networks","author":"Janssens R","year":"2018","unstructured":"Janssens R, Zeng G, Zheng G. Fully automatic segmentation of lumbar vertebrae from CT images using cascaded 3D fully convolutional networks. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE; 2018:893\u2013897. doi:10.1109\/ISBI.2018.8363715"},{"issue":"1","key":"b14","series-title":"Sci Rep","first-page":"22156","article-title":"Automatic vertebrae localization and segmentation in CT with a two-stage Dense-U-Net","volume":"11","author":"Cheng P","year":"2021","unstructured":"Cheng P, Yang Y, Yu H, He Y. Automatic vertebrae localization and segmentation in CT with a two-stage Dense-U-Net. Sci Rep. 2021;11(1):22156. doi:10.1038\/s41598-021-01296-1"},{"key":"b15","series-title":"VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","first-page":"124","article-title":"Coarse to fine vertebrae localization and segmentation with SpatialConfiguration-Net and U-Net","volume":"5","author":"Payer C","year":"2020","unstructured":"Payer C, Stern D, Bischof H, Urschler M. Coarse to fine vertebrae localization and segmentation with SpatialConfiguration-Net and U-Net. In: VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Vol 5. SciTePress;2020:124\u2013133. doi:10.5220\/0008975201240133"},{"issue":"16","key":"b16","series-title":"Diagnostics (Basel)","first-page":"2658","article-title":"An automated deep learning approach for spine segmentation and vertebrae recognition using computed tomography images","volume":"13","author":"Saeed MU","year":"2023","unstructured":"Saeed MU, Dikaios N, Dastgir A, Ali G, Hamid M, Hajjej F. An automated deep learning approach for spine segmentation and vertebrae recognition using computed tomography images. Diagnostics (Basel). 2023;13(16):2658. doi:10.3390\/diagnostics13162658"},{"key":"b17","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science","article-title":"Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks","volume":"9349","author":"Chen H","year":"2015","unstructured":"Chen H, Shen C, Qin J,  Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks. In: Navab N, Hornegger J, Wells W, Frangi A, eds. Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science. Vol 9349. Springer;2015. doi:10.1007\/978-3-319-24553-9_63"},{"key":"b18","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science","article-title":"Fast automatic vertebrae detection and localization in pathological CT scans\u2014a deep learning approach","volume":"9351","author":"Suzani A","year":"2015","unstructured":"Suzani A, Seitel A, Liu Y, Fels S, Rohling RN, Abolmaesumi P. Fast automatic vertebrae detection and localization in pathological CT scans\u2014a deep learning approach. In: Navab N, Hornegger J, Wells W, Frangi A, eds. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science. Vol 9351.Springer;2015. doi:10.1007\/978-3-319-24574-4_81"},{"key":"b19","series-title":"Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science","article-title":"Shape-aware deep convolutional neural network for vertebrae segmentation","volume":"10734","author":"Al Arif S","year":"2018","unstructured":"Al Arif S, Knapp K, Slabaugh G. Shape-aware deep convolutional neural network for vertebrae segmentation. In: Glocker B, Yao J, Vrtovec T, Frangi A, Zheng G, eds. Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science. Vol 10734. Springer;2018. doi:10.1007\/978-3-319-74113-0_2"},{"key":"b20","series-title":"Comput Methods Programs Biomed","first-page":"105833","article-title":"Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation","volume":"200","author":"Kim KC","year":"2021","unstructured":"Kim KC, Cho HC, Jang TJ, Choi JM, Seo JK. Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation. Comput Methods Programs Biomed. 2021;200:105833. doi:10.1016\/j.cmpb.2020.105833"},{"key":"b21","series-title":"Comput Biol Med","first-page":"105105","article-title":"Fully automated segmentation of lumbar bone marrow in sagittal, high-resolution T1-weighted magnetic resonance images using 2D U-NET","volume":"140","author":"Hwang EJ","year":"2022","unstructured":"Hwang EJ, Kim S, Jung JY. Fully automated segmentation of lumbar bone marrow in sagittal, high-resolution T1-weighted magnetic resonance images using 2D U-NET. Comput Biol Med. 2022;140:105105. doi:10.1016\/j.compbiomed.2021.105105"},{"issue":"3","key":"b22","series-title":"J Radiat Res Appl Sci","first-page":"100627","article-title":"Spinal magnetic resonance image segmentation based on U-net","volume":"16","author":"Wang Z","year":"2023","unstructured":"Wang Z, Xiao P, Tan H. Spinal magnetic resonance image segmentation based on U-net. J Radiat Res Appl Sci. 2023;16(3):100627. doi:10.1016\/j.jrras.2023.100627"},{"issue":"3","key":"b23","series-title":"J Digit Imaging","first-page":"513","article-title":"Automatic lumbar MRI detection and identification based on deep learning","volume":"32","author":"Zhou Y","year":"2019","unstructured":"Zhou Y, Liu Y, Chen Q, Gu G, Sui X. Automatic lumbar MRI detection and identification based on deep learning. J Digit Imaging. 2019;32(3):513\u2013520. doi:10.1007\/s10278-018-0130-7"},{"issue":"4","key":"b24","series-title":"J Digit Imaging","first-page":"406","article-title":"Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data","volume":"30","author":"Forsberg D","year":"2017","unstructured":"Forsberg D, Sj\u00f6blom E, Sunshine JL. Detection and labeling of vertebrae in MR images using deep learning with clinical annotations as training data. J Digit Imaging. 2017;30(4):406\u2013412. doi:10.1007\/s10278-017-9945-x"},{"key":"b25","series-title":"Proc Mach Learn Res","first-page":"403","article-title":"Deep Spine: Automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning","volume":"85","author":"Lu JT","year":"2018","unstructured":"Lu JT, Pedemonte S, Bizzo B,  Deep Spine: Automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning. Proc Mach Learn Res. 2018;85:403\u2013419."},{"issue":"1","key":"b26","series-title":"Radiology","first-page":"130","article-title":"Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI","volume":"300","author":"Hallinan JTPD","year":"2021","unstructured":"Hallinan JTPD, Zhu L, Yang K,  Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI. Radiology. 2021;300(1):130\u2013138. doi:10.1148\/radiol.2021204289"},{"issue":"3","key":"b27","series-title":"Eur Spine J","first-page":"941","article-title":"A neural network model for detection and classification of lumbar spinal stenosis on MRI","volume":"33","author":"Tumko V","year":"2024","unstructured":"Tumko V, Kim J, Uspenskaia N,  A neural network model for detection and classification of lumbar spinal stenosis on MRI. Eur Spine J. 2024;33(3):941\u2013948. doi:10.1007\/s00586-023-08089-2"},{"issue":"5","key":"b28","series-title":"Clin Spine Surg","first-page":"E180","article-title":"LSS-VGG16: diagnosis of lumbar spinal stenosis with deep learning","volume":"36","author":"Altun S","year":"2023","unstructured":"Altun S, Alkan A, Altun \u0130. LSS-VGG16: diagnosis of lumbar spinal stenosis with deep learning. Clin Spine Surg. 2023;36(5):E180\u2013E190. doi:10.1097\/BSD.0000000000001418"},{"issue":"5","key":"b29","series-title":"Eur Radiol","first-page":"3435","article-title":"Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI","volume":"33","author":"Bharadwaj UU","year":"2023","unstructured":"Bharadwaj UU, Christine M, Li S,  Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI. Eur Radiol. 2023;33(5):3435\u20133443. doi:10.1007\/s00330-023-09483-6"},{"issue":"2","key":"b30","series-title":"Asian Spine J","first-page":"207","article-title":"Diffusion tensor imaging of the spinal canal in quantitative assessment of patients with lumbar spinal canal stenosis","volume":"15","author":"Norimoto M","year":"2021","unstructured":"Norimoto M, Eguchi Y, Kanamoto H,  Diffusion tensor imaging of the spinal canal in quantitative assessment of patients with lumbar spinal canal stenosis. Asian Spine J. 2021;15(2):207\u2013215. doi:10.31616\/asj.2020.0026"},{"issue":"5","key":"b31","series-title":"PLoS One","first-page":"e0233633","article-title":"Clinical validity of two different grading systems for lumbar central canal stenosis: Schizas and Lee classification systems","volume":"15","author":"Ko YJ","year":"2020","unstructured":"Ko YJ, Lee E, Lee JW,  Clinical validity of two different grading systems for lumbar central canal stenosis: Schizas and Lee classification systems. PLoS One. 2020;15(5):e0233633. doi:10.1371\/journal.pone.0233633"},{"key":"b32","series-title":"GitHub","article-title":"Label Studio: Data labeling software","author":"Tkachenko M","unstructured":"Tkachenko M, Malyuk M, Holmanyuk A, Liubimov N. Label Studio: Data labeling software. GitHub. Accessed January 8, 2026. https:\/\/github.com\/heartexlabs\/label-studio"},{"key":"b33","series-title":"2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS)","first-page":"1449","article-title":"Development of ground truth data for automatic lumbar spine MRI image segmentation","author":"Natalia F","year":"2018","unstructured":"Natalia F, Meidia H, Afriliana N,  Development of ground truth data for automatic lumbar spine MRI image segmentation. In: 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC\/SmartCity\/DSS). IEEE; 2018:1449\u20131454. doi:10.1109\/HPCC\/SmartCity\/DSS.2018.00239"},{"key":"b34","series-title":"IEEE Access","first-page":"43487","article-title":"Boundary delineation of MRI images for lumbar spinal stenosis detection through semantic segmentation using deep neural networks","volume":"7","author":"Al-Kafri AS","year":"2019","unstructured":"Al-Kafri AS, Sudirman S, Hussain A,  Boundary delineation of MRI images for lumbar spinal stenosis detection through semantic segmentation using deep neural networks. IEEE Access. 2019;7:43487\u201343501. doi:10.1109\/ACCESS.2019.2908002"},{"key":"b35","series-title":"arXiv","article-title":"Battle of the backbones: a large-scale comparison of pretrained models across computer vision tasks","author":"Goldblum M","unstructured":"Goldblum M, Souri H, Ni R,  Battle of the backbones: a large-scale comparison of pretrained models across computer vision tasks. arXiv. Preprint published online November 20, 2023. doi:10.48550\/arXiv.2310.19909"},{"key":"b36","series-title":"2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)","first-page":"114","article-title":"A new approach to automatic disc localization in clinical lumbar MRI: combining machine learning with heuristics","author":"Ghosh S","year":"2012","unstructured":"Ghosh S, Malgireddy MR, Chaudhary V, Dhillon G. A new approach to automatic disc localization in clinical lumbar MRI: combining machine learning with heuristics. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE; 2012:114\u2013117. doi:10.1109\/ISBI.2012.6235497"},{"issue":"9","key":"b37","series-title":"IEEE Trans Biomed Eng","first-page":"2375","article-title":"Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM-based MRF","volume":"60","author":"Oktay AB","year":"2013","unstructured":"Oktay AB, Akgul YS. Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM-based MRF. IEEE Trans Biomed Eng. 2013;60(9):2375\u20132383. doi:10.1109\/TBME.2013.2256460"},{"key":"b38","series-title":"Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics","first-page":"219","article-title":"Vertebrae detection and labelling in lumbar MR images","volume":"17","author":"Lootus M","year":"2014","unstructured":"Lootus M, Kadir T, Zisserman A. Vertebrae detection and labelling in lumbar MR images. In: Yao J, Klinder T, Li S, eds. Computational Methods and Clinical Applications for Spine Imaging. Lecture Notes in Computational Vision and Biomechanics. Vol 17. Springer;2014:219\u2013230. doi:10.1007\/978-3-319-07269-2_19"},{"issue":"1","key":"b39","series-title":"IEEE Trans Med Imaging","first-page":"1","article-title":"Labeling of lumbar discs using both pixel-and object-level features with a two-level probabilistic model","volume":"30","author":"Alomari RS","year":"2010","unstructured":"Alomari RS, Corso JJ, Chaudhary V. Labeling of lumbar discs using both pixel-and object-level features with a two-level probabilistic model. IEEE Trans Med Imaging. 2010;30(1):1\u201310. doi:10.1109\/TMI.2010.2047403"},{"issue":"Pt 1","key":"b40","series-title":"Med Image Comput Comput Assist Interv","first-page":"202","article-title":"Lumbar disc localization and labeling with a probabilistic model on both pixel and object features","volume":"11","author":"Corso JJ","year":"2008","unstructured":"Corso JJ, Alomari RS, Chaudhary V. Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. Med Image Comput Comput Assist Interv. 2008;11(Pt 1):202-210. doi:10.1007\/978-3-540-85988-8_25"},{"issue":"4","key":"b41","series-title":"J Nucl Med","first-page":"488","article-title":"Introduction to radiomics","volume":"61","author":"Mayerhoefer ME","year":"2020","unstructured":"Mayerhoefer ME, Materka A, Langs G,  Introduction to radiomics. J Nucl Med. 2020;61(4):488\u2013495. doi:10.2967\/jnumed.118.222893"},{"issue":"12","key":"b42","series-title":"Spine (Phila Pa 1976)","first-page":"804","article-title":"Spinal stenosis grading in magnetic resonance imaging using deep convolutional neural networks","volume":"45","author":"Won D","year":"2020","unstructured":"Won D, Lee HJ, Lee SJ, Park SH. Spinal stenosis grading in magnetic resonance imaging using deep convolutional neural networks. Spine (Phila Pa 1976).2020;45(12):804\u2013812. doi:10.1097\/BRS.0000000000003377"},{"issue":"18","key":"b43","series-title":"Diagnostics (Basel)","first-page":"2975","article-title":"Nerve root compression analysis to find lumbar spine stenosis on MRI Using CNN","volume":"13","author":"Shahzadi T","year":"2023","unstructured":"Shahzadi T, Ali MU, Majeed F,  Nerve root compression analysis to find lumbar spine stenosis on MRI Using CNN. Diagnostics (Basel). 2023;13(18):2975. doi:10.3390\/diagnostics13182975"},{"key":"b44","series-title":"Med Image Anal","first-page":"63","article-title":"SpineNet: automated classification and evidence visualization in spinal MRIs","volume":"41","author":"Jamaludin A","year":"2017","unstructured":"Jamaludin A, Kadir T, Zisserman A. SpineNet: automated classification and evidence visualization in spinal MRIs. Med Image Anal. 2017;41:63\u201373. doi:10.1016\/j.media.2017.07.002"},{"issue":"1","key":"b45","series-title":"J Neurosurg Spine","first-page":"104","article-title":"Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs","volume":"37","author":"Kim T","year":"2022","unstructured":"Kim T, Kim YG, Park S,  Diagnostic triage in patients with central lumbar spinal stenosis using a deep learning system of radiographs. J Neurosurg Spine. 2022;37(1):104\u2013111. doi:10.3171\/2021.11.SPINE211136"}],"container-title":["Journal of Neurosurgery: Spine"],"original-title":[],"link":[{"URL":"https:\/\/thejns.org\/view\/journals\/j-neurosurg-spine\/44\/6\/article-p847.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/thejns.org\/downloadpdf\/journals\/j-neurosurg-spine\/44\/6\/article-p847.xml","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/thejns.org\/downloadpdf\/journals\/j-neurosurg-spine\/44\/6\/article-p847.xml","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T14:17:50Z","timestamp":1781187470000},"score":1,"resource":{"primary":{"URL":"https:\/\/thejns.org\/view\/journals\/j-neurosurg-spine\/44\/6\/article-p847.xml"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,27]]},"references-count":90,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3171\/2025.10.spine25878","relation":{},"ISSN":["1547-5654"],"issn-type":[{"value":"1547-5654","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,27]]}}}