{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T11:38:29Z","timestamp":1784547509913,"version":"3.55.0"},"reference-count":78,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","theclinics.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurosurgery Clinics of North America"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1016\/j.nec.2025.04.013","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T21:44:41Z","timestamp":1761687881000},"page":"623-638","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["Practical Applications of Artificial Intelligence in Spine Imaging"],"prefix":"10.1016","volume":"36","author":[{"given":"Upasana Upadhyay","family":"Bharadwaj","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cynthia T.","family":"Chin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharmila","family":"Majumdar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis","volume":"4","author":"Aggarwal","year":"2021","journal-title":"NPJ Digit Med"},{"issue":"5","key":"10.1016\/j.nec.2025.04.013_bib2","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1148\/rg.2021200210","article-title":"Deep Learning: An Update for Radiologists","volume":"41","author":"Cheng","year":"2021","journal-title":"Radiographics"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib3","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3348\/kjr.2019.0312","article-title":"Basics of Deep Learning: A Radiologist\u2019s Guide to Understanding Published Radiology Articles on Deep Learning","volume":"21","author":"Do","year":"2020","journal-title":"Korean J Radiol"},{"issue":"8","key":"10.1016\/j.nec.2025.04.013_bib4","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat Rev Cancer"},{"issue":"11","key":"10.1016\/j.nec.2025.04.013_bib5","doi-asserted-by":"crossref","first-page":"7998","DOI":"10.1007\/s00330-022-08784-6","article-title":"Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE)","volume":"32","author":"Kelly","year":"2022","journal-title":"Eur Radiol"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib6","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s13244-019-0832-5","article-title":"Deep learning workflow in radiology: a primer","volume":"11","author":"Montagnon","year":"2020","journal-title":"Insights Imaging"},{"issue":"5","key":"10.1016\/j.nec.2025.04.013_bib7","doi-asserted-by":"crossref","first-page":"3435","DOI":"10.1007\/s00330-023-09483-6","article-title":"Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI","volume":"33","author":"Bharadwaj","year":"2023","journal-title":"Eur Radiol"},{"key":"10.1016\/j.nec.2025.04.013_bib8","article-title":"Deep Learning for Multi-Tissue Segmentation and Fully Automatic Personalized Biomechanical Models from BACPAC Clinical Lumbar Spine MRI","author":"Hess","year":"2022","journal-title":"Pain Med"},{"issue":"2","key":"10.1016\/j.nec.2025.04.013_bib9","doi-asserted-by":"crossref","first-page":"e1204","DOI":"10.1002\/jsp2.1204","article-title":"Automatic detection and voxel-wise mapping of lumbar spine Modic changes with deep learning","volume":"5","author":"Gao","year":"2022","journal-title":"JOR Spine"},{"issue":"3","key":"10.1016\/j.nec.2025.04.013_bib10","doi-asserted-by":"crossref","first-page":"e212922","DOI":"10.1148\/radiol.212922","article-title":"Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability","volume":"306","author":"Almansour","year":"2023","journal-title":"Radiology"},{"issue":"5","key":"10.1016\/j.nec.2025.04.013_bib11","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics13050974","article-title":"Implementation of GAN-Based, Synthetic T2-Weighted Fat Saturated Images in the Routine Radiological Workflow Improves Spinal Pathology Detection","volume":"13","author":"Schlaeger","year":"2023","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib12","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1186\/s12880-022-00918-y","article-title":"Value assessment of artificial intelligence in medical imaging: a scoping review","volume":"22","author":"Fasterholdt","year":"2022","journal-title":"BMC Med Imag"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib13","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat Med"},{"issue":"10","key":"10.1016\/j.nec.2025.04.013_bib14","doi-asserted-by":"crossref","first-page":"e49177","DOI":"10.15252\/embr.201949177","article-title":"The limits of machine intelligence: Despite progress in machine intelligence, artificial general intelligence is still a major challenge","volume":"20","author":"Shevlin","year":"2019","journal-title":"EMBO Rep"},{"issue":"2","key":"10.1016\/j.nec.2025.04.013_bib15","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine Learning for Medical Imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"issue":"4","key":"10.1016\/j.nec.2025.04.013_bib16","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1007\/s42979-022-01166-1","article-title":"Survey of Supervised Learning for Medical Image Processing","volume":"3","author":"Aljuaid","year":"2022","journal-title":"SN Comput Sci"},{"issue":"9","key":"10.1016\/j.nec.2025.04.013_bib17","first-page":"1059","article-title":"A Tour of Unsupervised Deep Learning for Medical Image Analysis","volume":"17","author":"Raza","year":"2021","journal-title":"Curr Med Imaging"},{"key":"10.1016\/j.nec.2025.04.013_bib18","first-page":"1","article-title":"Machine learning and deep learning approach for medical image analysis: diagnosis to detection","author":"Rana","year":"2022","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"10.1016\/j.nec.2025.04.013_bib19","doi-asserted-by":"crossref","first-page":"e13898","DOI":"10.1002\/acm2.13898","article-title":"Reinforcement learning in medical image analysis: Concepts, applications, challenges, and future directions","volume":"24","author":"Hu","year":"2023","journal-title":"J Appl Clin Med Phys"},{"key":"10.1016\/j.nec.2025.04.013_bib20","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep Learning in Medical Image Analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu Rev Biomed Eng"},{"issue":"18","key":"10.1016\/j.nec.2025.04.013_bib21","doi-asserted-by":"crossref","DOI":"10.3390\/ijerph191811708","article-title":"Artificial Intelligence in Spinal Imaging: Current Status and Future Directions","volume":"19","author":"Cui","year":"2022","journal-title":"Int J Environ Res Publ Health"},{"issue":"4","key":"10.1016\/j.nec.2025.04.013_bib22","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: an overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"issue":"3","key":"10.1016\/j.nec.2025.04.013_bib23","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1148\/radiol.2018180547","article-title":"Convolutional Neural Networks for Radiologic Images: A Radiologist\u2019s Guide","volume":"290","author":"Soffer","year":"2019","journal-title":"Radiology"},{"issue":"5","key":"10.1016\/j.nec.2025.04.013_bib24","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?","volume":"35","author":"Tajbakhsh","year":"2016","journal-title":"IEEE Trans Med Imag"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12065-020-00540-3","article-title":"Convolutional neural networks in medical image understanding: a survey","volume":"15","author":"Sarvamangala","year":"2022","journal-title":"Evol Intell"},{"issue":"8","key":"10.1016\/j.nec.2025.04.013_bib26","doi-asserted-by":"crossref","DOI":"10.3390\/jimaging7080155","article-title":"Dataset Growth in Medical Image Analysis Research","volume":"7","author":"Kiryati","year":"2021","journal-title":"J Imaging"},{"issue":"6","key":"10.1016\/j.nec.2025.04.013_bib27","article-title":"Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5T MRI","volume":"10","author":"Kashiwagi","year":"2021","journal-title":"Acta Radiol Open"},{"issue":"9","key":"10.1016\/j.nec.2025.04.013_bib28","doi-asserted-by":"crossref","first-page":"6167","DOI":"10.1007\/s00330-022-08708-4","article-title":"Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI","volume":"32","author":"Sun","year":"2022","journal-title":"Eur Radiol"},{"key":"10.1016\/j.nec.2025.04.013_bib29","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.mri.2021.10.038","article-title":"Improvement of peripheral nerve visualization using a deep learning-based MR reconstruction algorithm","volume":"85","author":"Zochowski","year":"2022","journal-title":"Magn Reson Imaging"},{"issue":"11","key":"10.1016\/j.nec.2025.04.013_bib30","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.3348\/kjr.2021.0140","article-title":"Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT","volume":"22","author":"Yeoh","year":"2021","journal-title":"Korean J Radiol"},{"issue":"2","key":"10.1016\/j.nec.2025.04.013_bib31","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.diii.2022.08.004","article-title":"Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study","volume":"104","author":"Greffier","year":"2023","journal-title":"Diagn Interv Imaging"},{"issue":"7","key":"10.1016\/j.nec.2025.04.013_bib32","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1007\/s00256-022-04268-2","article-title":"Rapid lumbar MRI protocol using 3D imaging and deep learning reconstruction","volume":"52","author":"Chazen","year":"2023","journal-title":"Skeletal Radiol"},{"issue":"10","key":"10.1016\/j.nec.2025.04.013_bib33","doi-asserted-by":"crossref","first-page":"1776","DOI":"10.3174\/ajnr.A5543","article-title":"Deep Learning in Neuroradiology","volume":"39","author":"Zaharchuk","year":"2018","journal-title":"AJNR Am J Neuroradiol"},{"issue":"Suppl 1","key":"10.1016\/j.nec.2025.04.013_bib34","doi-asserted-by":"crossref","first-page":"S149","DOI":"10.1093\/pm\/pnad035","article-title":"Technology and Tool Development for BACPAC: Qualitative and Quantitative Analysis of Accelerated Lumbar Spine MRI with Deep-Learning Based Image Reconstruction at 3T","volume":"24","author":"Han","year":"2023","journal-title":"Pain Med"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib35","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s00062-021-01121-2","article-title":"Deep Learning Image Processing Enables 40% Faster Spinal MR Scans Which Match or Exceed Quality of Standard of Care : A Prospective Multicenter Multireader Study","volume":"32","author":"Bash","year":"2022","journal-title":"Clin Neuroradiol"},{"key":"10.1016\/j.nec.2025.04.013_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.spinee.2023.06.399","article-title":"Imaging evaluation of a proposed 3D generative model for MRI to CT translation in the lumbar spine","author":"Roberts","year":"2023","journal-title":"Spine J"},{"issue":"8","key":"10.1016\/j.nec.2025.04.013_bib37","doi-asserted-by":"crossref","first-page":"5882","DOI":"10.1007\/s00330-023-09512-4","article-title":"Synthetic T2-weighted fat sat based on a generative adversarial network shows potential for scan time reduction in spine imaging in a multicenter test dataset","volume":"33","author":"Schlaeger","year":"2023","journal-title":"Eur Radiol"},{"issue":"8","key":"10.1016\/j.nec.2025.04.013_bib38","doi-asserted-by":"crossref","first-page":"987","DOI":"10.3174\/ajnr.A7920","article-title":"Deep Learning-Generated Synthetic MR Imaging STIR Spine Images Are Superior in Image Quality and Diagnostically Equivalent to Conventional STIR: A Multicenter, Multireader Trial","volume":"44","author":"Tanenbaum","year":"2023","journal-title":"AJNR Am J Neuroradiol"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib39","doi-asserted-by":"crossref","first-page":"9482","DOI":"10.1038\/s41598-021-89102-w","article-title":"2-step deep learning model for landmarks localization in spine radiographs","volume":"11","author":"Cina","year":"2021","journal-title":"Sci Rep"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib40","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1148\/radiol.2021204289","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","year":"2021","journal-title":"Radiology"},{"issue":"2","key":"10.1016\/j.nec.2025.04.013_bib41","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/TMI.2019.2927289","article-title":"Vertebrae Identification and Localization Utilizing Fully Convolutional Networks and a Hidden Markov Model","volume":"39","author":"Chen","year":"2020","journal-title":"IEEE Trans Med Imag"},{"key":"10.1016\/j.nec.2025.04.013_bib42","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.media.2017.07.002","article-title":"SpineNet: Automated classification and evidence visualization in spinal MRIs","volume":"41","author":"Jamaludin","year":"2017","journal-title":"Med Image Anal"},{"issue":"7","key":"10.1016\/j.nec.2025.04.013_bib43","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.compmedimag.2014.03.005","article-title":"Supervised methods for detection and segmentation of tissues in clinical lumbar MRI","volume":"38","author":"Ghosh","year":"2014","journal-title":"Comput Med Imag Graph"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib44","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1038\/s41597-021-01060-0","article-title":"A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data","volume":"8","author":"Liebl","year":"2021","journal-title":"Sci Data"},{"key":"10.1016\/j.nec.2025.04.013_bib45","doi-asserted-by":"crossref","first-page":"1044525","DOI":"10.3389\/fpubh.2023.1044525","article-title":"Spinal disease diagnosis assistant based on MRI images using deep transfer learning methods","volume":"11","author":"Xuan","year":"2023","journal-title":"Front Public Health"},{"key":"10.1016\/j.nec.2025.04.013_bib46","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Broz T. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv. 2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.1016\/j.nec.2025.04.013_bib47","doi-asserted-by":"crossref","unstructured":"Chen L-C, Papandreou G, Kokkinow I, Murphy K. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. arXiv 2017.","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"10.1016\/j.nec.2025.04.013_bib48","first-page":"4259471","article-title":"Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net","volume":"2022","author":"Wang","year":"2022","journal-title":"Comput Intell Neurosci"},{"key":"10.1016\/j.nec.2025.04.013_bib49","first-page":"16","article-title":"Deep spine: automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning","volume":"85","author":"Lu","year":"2018","journal-title":"Proceedings of Machine Learning Research"},{"issue":"5","key":"10.1016\/j.nec.2025.04.013_bib50","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1007\/s00586-017-4956-3","article-title":"ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist","volume":"26","author":"Jamaludin","year":"2017","journal-title":"Eur Spine J"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib51","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1186\/s12891-020-3164-1","article-title":"Could automated machine-learned MRI grading aid epidemiological studies of lumbar spinal stenosis? Validation within the Wakayama spine study","volume":"21","author":"Ishimoto","year":"2020","journal-title":"BMC Muscoskel Disord"},{"issue":"10","key":"10.1016\/j.nec.2025.04.013_bib52","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1007\/s00586-019-06119-6","article-title":"Measuring and reporting of vertebral endplate bone marrow lesions as seen on MRI (Modic changes): recommendations from the ISSLS Degenerative Spinal Phenotypes Group","volume":"28","author":"Fields","year":"2019","journal-title":"Eur Spine J"},{"issue":"15","key":"10.1016\/j.nec.2025.04.013_bib53","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1097\/BRS.0b013e3181ecf283","article-title":"Quantitative measures of modic changes in lumbar spine magnetic resonance imaging: intra- and inter-rater reliability","volume":"36","author":"Wang","year":"2011","journal-title":"Spine"},{"issue":"1060","key":"10.1016\/j.nec.2025.04.013_bib54","doi-asserted-by":"crossref","first-page":"20150650","DOI":"10.1259\/bjr.20150650","article-title":"Modic changes in lumbar spine: prevalence and distribution patterns of end plate oedema and end plate sclerosis","volume":"89","author":"Xu","year":"2016","journal-title":"Br J Radiol"},{"issue":"11","key":"10.1016\/j.nec.2025.04.013_bib55","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1007\/s00586-008-0770-2","article-title":"Vertebral endplate signal changes (Modic change): a systematic literature review of prevalence and association with non-specific low back pain","volume":"17","author":"Jensen","year":"2008","journal-title":"Eur Spine J"},{"issue":"4","key":"10.1016\/j.nec.2025.04.013_bib56","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1097\/BRS.0000000000004532","article-title":"Differentiating Magnetic Resonance Images of Pyogenic Spondylitis and Spinal Modic Change Using a Convolutional Neural Network","volume":"48","author":"Mukaihata","year":"2023","journal-title":"Spine (Phila Pa 1976)"},{"key":"10.1016\/j.nec.2025.04.013_bib57","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.neuroimage.2018.09.081","article-title":"Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks","volume":"184","author":"Gros","year":"2019","journal-title":"Neuroimage"},{"key":"10.1016\/j.nec.2025.04.013_bib58","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compbiomed.2018.05.011","article-title":"Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans","volume":"98","author":"Tomita","year":"2018","journal-title":"Comput Biol Med"},{"issue":"3","key":"10.1016\/j.nec.2025.04.013_bib59","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1148\/radiol.2017162100","article-title":"Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images","volume":"284","author":"Burns","year":"2017","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib60","doi-asserted-by":"crossref","first-page":"20031","DOI":"10.1038\/s41598-020-76866-w","article-title":"Artificial intelligence for the detection of vertebral fractures on plain spinal radiography","volume":"10","author":"Murata","year":"2020","journal-title":"Sci Rep"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib61","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1148\/radiol.2015142346","article-title":"Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT","volume":"278","author":"Burns","year":"2016","journal-title":"Radiology"},{"issue":"7","key":"10.1016\/j.nec.2025.04.013_bib62","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.3174\/ajnr.A7094","article-title":"CT Cervical Spine Fracture Detection Using a Convolutional Neural Network","volume":"42","author":"Small","year":"2021","journal-title":"AJNR Am J Neuroradiol"},{"issue":"Pt A","key":"10.1016\/j.nec.2025.04.013_bib63","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.neuroimage.2016.10.009","article-title":"SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data","volume":"145","author":"De Leener","year":"2017","journal-title":"Neuroimage"},{"issue":"4","key":"10.1016\/j.nec.2025.04.013_bib64","first-page":"737","article-title":"Convolutional Neural Network-Based Automated Segmentation of the Spinal Cord and Contusion Injury: Deep Learning Biomarker Correlates of Motor Impairment in Acute Spinal Cord Injury","volume":"40","author":"McCoy","year":"2019","journal-title":"AJNR Am J Neuroradiol"},{"issue":"2","key":"10.1016\/j.nec.2025.04.013_bib65","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/23808993.2019.1585805","article-title":"Deep learning and radiomics in precision medicine","volume":"4","author":"Parekh","year":"2019","journal-title":"Expert Rev Precis Med Drug Dev"},{"issue":"16","key":"10.1016\/j.nec.2025.04.013_bib66","doi-asserted-by":"crossref","DOI":"10.3390\/cancers14164025","article-title":"Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis","volume":"14","author":"Ong","year":"2022","journal-title":"Cancers"},{"issue":"9","key":"10.1016\/j.nec.2025.04.013_bib67","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1109\/TMI.2021.3080991","article-title":"Two-Stream Compare and Contrast Network for Vertebral Compression Fracture Diagnosis","volume":"40","author":"Feng","year":"2021","journal-title":"IEEE Trans Med Imag"},{"issue":"7","key":"10.1016\/j.nec.2025.04.013_bib68","doi-asserted-by":"crossref","first-page":"5060","DOI":"10.1007\/s00330-023-09713-x","article-title":"Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist\u2019s assessment","volume":"33","author":"Liu","year":"2023","journal-title":"Eur Radiol"},{"issue":"5","key":"10.1016\/j.nec.2025.04.013_bib69","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1007\/s00586-019-05944-z","article-title":"Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach","volume":"28","author":"Galbusera","year":"2019","journal-title":"Eur Spine J"},{"issue":"1_suppl","key":"10.1016\/j.nec.2025.04.013_bib70","doi-asserted-by":"crossref","first-page":"23S","DOI":"10.1177\/2192568220961353","article-title":"Applications of Machine Learning to Imaging of Spinal Disorders: Current Status and Future Directions","volume":"11","author":"Merali","year":"2021","journal-title":"Global Spine J"},{"issue":"8","key":"10.1016\/j.nec.2025.04.013_bib71","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1002\/jor.25213","article-title":"Institution-wide shape analysis of 3D spinal curvature and global alignment parameters","volume":"40","author":"Iriondo","year":"2022","journal-title":"J Orthop Res"},{"key":"10.1016\/j.nec.2025.04.013_bib72","doi-asserted-by":"crossref","unstructured":"Ooi BC, Tan KL, Wang S, et al. SINGA: A Distributed Deep Learning Platform. Mm\u201915: Proceedings of the 2015 Acm Multimedia Conference. 2015:685-688.","DOI":"10.1145\/2733373.2807410"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib73","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1148\/radiol.220076","article-title":"Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI","volume":"305","author":"Lim","year":"2022","journal-title":"Radiology"},{"issue":"9","key":"10.1016\/j.nec.2025.04.013_bib74","doi-asserted-by":"crossref","first-page":"e599","DOI":"10.1016\/S2589-7500(21)00132-1","article-title":"Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review","volume":"3","author":"Young","year":"2021","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.nec.2025.04.013_bib75","doi-asserted-by":"crossref","first-page":"110726","DOI":"10.1016\/j.ejrad.2023.110726","article-title":"The role of Artificial intelligence in the assessment of the spine and spinal cord","volume":"161","author":"Martin-Noguerol","year":"2023","journal-title":"Eur J Radiol"},{"issue":"1","key":"10.1016\/j.nec.2025.04.013_bib76","doi-asserted-by":"crossref","first-page":"e1044","DOI":"10.1002\/jsp2.1044","article-title":"Artificial intelligence and machine learning in spine research","volume":"2","author":"Galbusera","year":"2019","journal-title":"JOR Spine"},{"issue":"5","key":"10.1016\/j.nec.2025.04.013_bib77","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s13244-018-0645-y","article-title":"Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States","volume":"9","author":"Pesapane","year":"2018","journal-title":"Insights Imaging"},{"issue":"2133","key":"10.1016\/j.nec.2025.04.013_bib78","article-title":"Ethical governance is essential to building trust in robotics and artificial intelligence systems","volume":"376","author":"Winfield","year":"2018","journal-title":"Philos Trans A Math Phys Eng Sci"}],"container-title":["Neurosurgery Clinics of North America"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1042368025000415?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1042368025000415?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T23:11:16Z","timestamp":1761779476000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1042368025000415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":78,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["S1042368025000415"],"URL":"https:\/\/doi.org\/10.1016\/j.nec.2025.04.013","relation":{},"ISSN":["1042-3680"],"issn-type":[{"value":"1042-3680","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Practical Applications of Artificial Intelligence in Spine Imaging","name":"articletitle","label":"Article Title"},{"value":"Neurosurgery Clinics of North America","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.nec.2025.04.013","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}