Advancing computer vision in spine surgery: a comprehensive review of current applications and future directions

Abstract

Artificial intelligence is rapidly reshaping healthcare, with computer vision enabling automated interpretation of imaging across a wide range of clinical applications. Given its heavy reliance on imaging across perioperative settings, spine surgery is particularly well-suited for the integration of computer vision technologies. At the same time, spine surgery is among the fastest-growing and most resource-intensive specialties, with rising demands that underscore the need for technologies capable of enhancing precision, efficiency, and safety. In this narrative review, we will synthesize current applications of computer vision across the spine surgery workflow, outline key barriers for implementation, and discuss future directions for translating these tools into widespread clinical practice. Computer vision tools span a spectrum of maturity, with some already achieving clinical deployment for spinopelvic parameter measurement, pathology detection, and surgical planning, while intraoperative applications represent the most actively developing frontier. These innovations may redefine the standard of care in spine surgery, enabling a new era of surgical performance and data-informed decision-making.

Keywords

Computer vision / artificial intelligence / spine surgery / diagnostics / intraoperative imaging

References

[1] Weiss AJ,Andrews RM. Characteristics of operating room procedures in U.S. hospitals, 2011. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville (MD): Agency for Healthcare Research and Quality (US); 2006. Available from https://pubmed.ncbi.nlm.nih.gov/24716251/ [accessed 16 April 2026].

[2] Martin BI,Spina N,Lawrence B.Trends in lumbar fusion procedure rates and associated hospital costs for degenerative spinal diseases in the United States, 2004 to 2015.Spine2019;44:369-76

[3] Philipp LR,Mahtabfar A,Gonzalez GA.Achieving value in spine surgery: 10 major cost contributors.Global Spine J2021;11:14S-22S PMCID:PMC8076814

[4] Mascagni P,Sestini L.Computer vision in surgery: from potential to clinical value.NPJ Digit Med2022;5:163 PMCID:PMC9616906

[5] Maier-Hein L,Ross T.Heidelberg colorectal data set for surgical data science in the sensor operating room.Sci Data2021;8:101 PMCID:PMC8042116

[6] Zia A,Nespolo RG. Intuitive surgical SurgToolLoc challenge results: 2022-2023. arXiv 2023;arXiv:2305.07152. Available from: http://arxiv.org/abs/2305.07152 [accessed 16 April 2026].

[7] Wybier M.Musculoskeletal imaging in progress: the EOS imaging system.Joint Bone Spine2013;80:238-43

[8] Santiago F, Láinez Ramos-Bossini AJ, Wáng YXJ, Martínez Barbero JP, García Espinosa J, Martínez Martínez A. The value of magnetic resonance imaging and computed tomography in the study of spinal disorders.Quant Imaging Med Surg2022;12:3947-86 PMCID:PMC9246762

[9] Chai J,Li A.Deep learning in computer vision: a critical review of emerging techniques and application scenarios.Mach Learn Appl2021;6:100134

[10] Han K,Chen H.A survey on vision transformer.IEEE Trans Pattern Anal Mach Intell2023;45:87-110

[11] Esteva A,Yeung S.Deep learning-enabled medical computer vision.NPJ Digit Med2021;4:5 PMCID:PMC7794558

[12] Minaee S,Porikli F,Kehtarnavaz N. Image segmentation using deep learning: a survey. arXiv 2020;arXiv:2001.05566. Available from: http://arxiv.org/abs/2001.05566 [accessed 16 April 2026].

[13] Zhou N,Wang Y,Zhou L.Review of deep learning models for spine segmentation. In: Proceedings of the 2022 International Conference on Multimedia Retrieval; 2022 Jun 27-30; Newark, NJ, USA. New York: ACM; 2022. pp. 498-507.

[14] Bhairnallykar ST.Exploration of image segmentation: a comprehensive review of traditional segmentation techniques. In: 2023 7th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech); 2023 Dec 18-20; Kolkata, India. New York: IEEE; 2023. pp. 1-6.

[15] Zamora G,Long LR.Hierarchical segmentation of vertebrae from X-ray images. In: Sonka M, Fitzpatrick JM, Editors. Medical Imaging 2003: Image Processing; 2003; San Diego, CA, USA. Bellingham: SPIE; 2003.

[16] Koompairojn S,Bhadrakom C.Automatic classification system for lumbar spine X-ray images. In: 19th IEEE Symposium on Computer-Based Medical Systems (CBMS’06); 2006 Jun 22-23; Salt Lake City, UT, USA. New York: IEEE; 2006. pp. 213-8.

[17] Lecron F,Mahmoudi S.Cervical spine mobility analysis on radiographs: a fully automatic approach.Comput Med Imaging Graph2012;36:634-42

[18] Li Y, Liang W, Zhang Y, An H, Tan J. Automatic lumbar vertebrae detection based on feature fusion deep learning for partial occluded C-arm X-ray images.Annu Int Conf IEEE Eng Med Biol Soc2016;2016:647-50

[19] Al Arif SMMR, Knapp K, Slabaugh G. Fully automatic cervical vertebrae segmentation framework for X-ray images.Comput Methods Programs Biomed2018;157:95-111

[20] Cho BH,Cheung ZB.Automated measurement of lumbar lordosis on radiographs using machine learning and computer vision.Global Spine J2020;10:611-8 PMCID:PMC7359685

[21] Shin Y,Lee YH.Temporal trends in cervical spine curvature of South Korean adults assessed by deep learning system segmentation, 2006-2018.JAMA Netw Open2020;3:e2020961 PMCID:PMC7563081

[22] Kim KC,Jang TJ,Seo JK.Automatic detection and segmentation of lumbar vertebrae from X-ray images for compression fracture evaluation.Comput Methods Programs Biomed2021;200:105833

[23] Chen Y,Readie A.VertXNet: an ensemble method for vertebral body segmentation and identification from cervical and lumbar spinal X-rays.Sci Rep2024;14:3341 PMCID:PMC10858234

[24] Bertels J,Berman M.Optimizing the Dice score and Jaccard index for medical image segmentation: theory and practice. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap PT, Khan A, Editors. Medical Image Computing and Computer Assisted Intervention - MICCAI 2019; 2019 Oct 13-17; Shenzhen, China. Cham: Springer; 2019. pp. 92-100.

[25] Ronneberger O,Brox T. U-Net: convolutional networks for biomedical image segmentation. arXiv 2015;arXiv:1505.04597. Available from: http://arxiv.org/abs/1505.04597 [accessed 16 April 2026].

[26] Janssens R,Zheng G. Fully automatic segmentation of lumbar vertebrae from CT images using cascaded 3D Fully Convolutional Networks. arXiv 2017;arXiv:1712.01509. Available from: http://arxiv.org/abs/1712.01509 [accessed 16 April 2026].

[27] Lessmann N,de Jong PA.Iterative fully convolutional neural networks for automatic vertebra segmentation and identification.Med Image Anal2019;53:142-55

[28] Sekuboyina A,Bayat A.VerSe: a Vertebrae labelling and segmentation benchmark for multi-detector CT images.Med Image Anal2021;73:102166

[29] Payer C,Bischof H.Coarse to fine vertebrae localization and segmentation with SpatialConfiguration-net and U-net. In: Farinella GM, Radeva P, Braz J, Editors. Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications; 2020 Feb 27-29; Valletta, Malta. Setúbal: SciTePress; 2020. pp. 124-33.

[30] Tao R,Zheng G.Spine-transformers: vertebra labeling and segmentation in arbitrary field-of-view spine CTs via 3D transformers.Med Image Anal2022;75:102258

[31] van der Graaf JW,Buckens CFM.Lumbar spine segmentation in MR images: a dataset and a public benchmark.Sci Data2024;11:264 PMCID:PMC10908819

[32] Zhang Z,Fan G.SpineMamba: enhancing 3D spinal segmentation in clinical imaging through residual visual Mamba layers and shape priors.Comput Med Imaging Graph2025;123:102531

[33] Liebl H,Sekuboyina A.A computed tomography vertebral segmentation dataset with anatomical variations and multi-vendor scanner data.Sci Data2021;8:284 PMCID:PMC8553749

[34] Sa R,Wiegand R.Intervertebral disc detection in X-ray images using faster R-CNN.Annu Int Conf IEEE Eng Med Biol Soc2017;2017:564-7

[35] Lemay A,Zhuo Z.Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning.Neuroimage Clin2021;31:102766 PMCID:PMC8350366

[36] Li X,Xu Y.VerFormer: vertebrae-aware transformer for automatic spine segmentation from CT images.Diagnostics2024;14:1859 PMCID:PMC11393940

[37] Kirillov A,Ravi N. Segment anything. arXiv 2023;arXiv:2304.02643. Available from: http://arxiv.org/abs/2304.02643 [accessed 16 April 2026].

[38] Ma J,Li F,You C.Segment anything in medical images.Nat Commun2024;15:654 PMCID:PMC10803759

[39] Seoud L,Labelle H,Cheriet F.Prediction of scoliosis curve type based on the analysis of trunk surface topography. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2010 Apr 14-17; Rotterdam, Netherlands. New York: IEEE; 2010. pp. 408-11.

[40] Lin Y,Ma K,Zheng Y.Seg4Reg networks for automated spinal curvature estimation. In: Cai Y, Wang L, Audette M, Zheng G, Li S, Editors. Proceedings of the 7th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019; 2019 Oct 17; Shenzhen, China. Cham: Springer; 2020. pp. 69-74.

[41] Chen K,Li Y,Wei S.Accurate automated keypoint detections for spinal curvature estimation. In: Cai Y, Wang L, Audette M, Zheng G, Li S, Editors. Proceedings of the 7th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019; 2019 Oct 17; Shenzhen, China. Cham: Springer; 2020. pp. 63-8.

[42] Dubost F,Renaudier A.Automated estimation of the spinal curvature via spine centerline extraction with ensembles of cascaded neural networks. In: Cai Y, Wang L, Audette M, Zheng G, Li S, Editors. Proceedings of the 7th International Workshop and Challenge on Computational Methods and Clinical Applications for Spine Imaging, CSI 2019; 2019 Oct 17; Shenzhen, China. Cham: Springer; 2020. pp. 88-94.

[43] Lin Y,Ma K. Seg4Reg+: consistency learning between spine segmentation and Cobb angle regression. arXiv 2022;arXiv:2208.12462. Available from: http://arxiv.org/abs/2208.12462 [accessed 16 April 2026].

[44] Alukaev D,Mustafaev T,Ibragimov B.A deep learning framework for vertebral morphometry and Cobb angle measurement with external validation.Eur Spine J2022;31:2115-24

[45] Chen K,Klestil T,Lepenik C.Fully automated measurement of Cobb angles in coronal plane spine radiographs.J Clin Med2024;13:4122 PMCID:PMC11278017

[46] Orosz LD,Jazini E.Novel artificial intelligence algorithm: an accurate and independent measure of spinopelvic parameters.J Neurosurg Spine2022;37:893-901

[47] Harake ES,Jiang C.Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters.J Neurosurg Spine2024;41:88-96 PMCID:PMC11494712

[48] Joshi RS,Jiang C.Validation of a novel artificial intelligence model (SpinePose) to automatically and accurately predict spinopelvic parameters using scoliosis radiographs in an external cohort.Neurosurg Focus2025;58:E10

[49] Kang DH,Kim ST.Automated measurement of pelvic parameters using convolutional neural network in complex spinal deformities: overcoming challenges in coronal deformity cases.Spine J2025;25:1688-97

[50] Grover P,Caspari C.Can artificial intelligence support or even replace physicians in measuring sagittal balance?.Eur Spine J2022;31:1943-51

[51] Suri A,Kargilis D.Conquering the Cobb angle: a deep learning algorithm for automated, hardware-invariant measurement of Cobb angle on radiographs in patients with scoliosis.Radiol Artif Intell2023;5:e220158 PMCID:PMC10388214

[52] Löchel J,Dreischarf M.Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity.Eur Spine J2024;33:4119-24

[53] Vogt S,Grover P.Novel AI-based algorithm for the automated measurement of cervical sagittal balance parameters. A validation study on pre- and postoperative radiographs of 129 patients.Global Spine J2025;15:1155-65 PMCID:PMC11571950

[54] Derkatch S,Kimelman D,Davidson JM.Identification of vertebral fractures by convolutional neural networks to predict nonvertebral and hip fractures: a registry-based cohort study of dual X-ray absorptiometry.Radiology2019;293:405-11

[55] Li YC,Horng-Shing Lu H,Chang MC.Can a deep-learning model for the automated detection of vertebral fractures approach the performance level of human subspecialists?.Clin Orthop Relat Res2021;479:1598-612 PMCID:PMC8208416

[56] Shen L,Hu S.Using artificial intelligence to diagnose osteoporotic vertebral fractures on plain radiographs.J Bone Miner Res2023;38:1278-87

[57] Liu B,Feng S,Zhang Y.Benign vs malignant vertebral compression fractures with MRI: a comparison between automatic deep learning network and radiologist’s assessment.Eur Radiol2023;33:5060-8

[58] Jamaludin A,Zisserman A.SpineNet: automated classification and evidence visualization in spinal MRIs.Med Image Anal2017;41:63-73

[59] Merali Z,Badhiwala JH,Wilson JR.A deep learning model for detection of cervical spinal cord compression in MRI scans.Sci Rep2021;11:10473 PMCID:PMC8131597

[60] Hallinan JTPD,Yang K,Algazwi DAR.Deep learning model for automated detection and classification of central canal, lateral recess, and neural foraminal stenosis at lumbar spine MRI.Radiology2021;300:130-8

[61] Nguyen HT,Nguyen NT.VinDr-SpineXR: a deep learning framework for spinal lesions detection and classification from radiographs. arXiv 2021;arXiv:2106.12930. Available from: http://arxiv.org/abs/2106.12930 [accessed 16 April 2026]..

[62] Maki S,Horikoshi T.A deep convolutional neural network with performance comparable to radiologists for differentiating between spinal schwannoma and meningioma.Spine2020;45:694-700

[63] Haim O,Gabay S.Differentiating spinal pathologies by deep learning approach.Spine J2024;24:297-303

[64] Zhuo Z,Duan Y.Automated classification of intramedullary spinal cord tumors and inflammatory demyelinating lesions using deep learning.Radiol Artif Intell2022;4:e210292 PMCID:PMC9745442

[65] Huang KT,See AP.A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware.J Neurosurg Spine2019;31:844-50

[66] Yang HS,Kim S.Deep learning application in spinal implant identification.Spine2021;46:E318-24

[67] Dutt R,Phen HM.Automatic localization and brand detection of cervical spine hardware on radiographs using weakly supervised machine learning.Radiol Artif Intell2022;4:e210099 PMCID:PMC8980883

[68] Park S,Chang MC,Yang JJ.Assessment of fusion after anterior cervical discectomy and fusion using convolutional neural network algorithm.Spine2022;47:1645-50

[69] Schwartz JT,Arvind V.Identification of anterior cervical spinal instrumentation using a smartphone application powered by machine learning.Spine2022;47:E407-14

[70] Anand A,McDonald MF,Xu DS.A computer vision approach to identifying the manufacturer of posterior thoracolumbar instrumentation systems.J Neurosurg Spine2023;38:417-24

[71] Chun KS,Choi H,Lee S.Patch-wise approach with vision transformer for detecting implant failure in spinal radiography.J Imaging Inform Med2025;

[72] Sun H,Deng L.Development and validation of interpretable machine learning models incorporating paraspinal muscle quality to predict cage subsidence risk following posterior lumbar interbody fusion.Spine2025;50:1375-85 PMCID:PMC12456198

[73] Waranusast R,Weerakul S,Laoruengthana A.Fully automated pedicle screw manufacturer identification in plain radiograph with deep learning methods.Eur Spine J2025;34:3940-9

[74] De Silva T,Perdomo-Pantoja A.SpineCloud: image analytics for predictive modeling of spine surgery outcomes.J Med Imaging2020;7:031502 PMCID:PMC7026518

[75] Saravi B,Ülkümen S.Clinical and radiomics feature-based outcome analysis in lumbar disc herniation surgery.BMC Musculoskelet Disord2023;24:791 PMCID:PMC10557221

[76] Wu J,Zhang H,Shen X.Predicting functional outcome after open lumbar fusion surgery: a retrospective multicenter cohort study.Eur J Radiol2025;182:111836

[77] Durand WM,Hamilton DK.International Spine Study Group (ISSG)Artificial intelligence clustering of adult spinal deformity sagittal plane morphology predicts surgical characteristics, alignment, and outcomes.Eur Spine J2021;30:2157-66

[78] Rudisill SS,Barajas JN.Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion.Eur Spine J2022;31:2104-14

[79] Johnson GW,Ali MA.Artificial intelligence to preoperatively predict proximal junction kyphosis following adult spinal deformity surgery: soft tissue imaging may be necessary for accurate models.Spine2023;48:1688-95 PMCID:PMC11101214

[80] Brigato P,De Salvatore S.Harnessing machine learning to predict and prevent proximal junctional kyphosis and failure in adult spinal deformity surgery: a systematic review.Brain Spine2025;5:104273 PMCID:PMC12145805

[81] Zheng B,Ma K,Liang Y.Radiomics-based machine learning model integrating preoperative vertebral computed tomography and clinical features to predict cage subsidence after single-level anterior cervical discectomy and fusion with a zero-profile anchored spacer.J Orthop Surg Res2025;20:851 PMCID:PMC12465803

[82] Bishara A,Warman A.Artificial intelligence automated measurements of spinopelvic parameters in adult spinal deformity - a systematic review.Spine Deform2025;13:1289-304

[83] Smith JS,Ames CP.International Spine Study GroupAssessment of symptomatic rod fracture after posterior instrumented fusion for adult spinal deformity.Neurosurgery2012;71:862-7

[84] Hamilton DK,Passias P.International Spine Study GroupThe fate of patients with adult spinal deformity incurring rod fracture after thoracolumbar fusion.World Neurosurg2017;106:905-11

[85] Schwab FJ,Bess S.International Spine Study Group (ISSG)Radiographical spinopelvic parameters and disability in the setting of adult spinal deformity: a prospective multicenter analysis.Spine2013;38:E803-12

[86] Divi SN,Bowles DR.How do spinopelvic parameters influence patient-reported outcome measurements after lumbar decompression?.Spine J2020;20:1610-7

[87] Ye J,Farooqi AS.Predictive role of global spinopelvic alignment and upper instrumented vertebra level in symptomatic proximal junctional kyphosis in adult spinal deformity.J Neurosurg Spine2023;39:774-84

[88] Wondra JP 2nd,Greenberg J.Validation of adult spinal deformity surgical outcome prediction tools in adult symptomatic lumbar scoliosis.Spine2023;48:21-8 PMCID:PMC9771887

[89] Schönnagel L,Vu-Han TL.Predicting postoperative outcomes in lumbar spinal fusion: development of a machine learning model.Spine J2024;24:239-49

[90] Parisien A,ElSayed MSA.Subsidence of spinal fusion cages: a systematic review.Int J Spine Surg2022;16:1103-18 PMCID:PMC9807049

[91] Bui TT,Yahanda AT,Ruiz-Cardozo M.How much variance exists among published definitions of proximal junctional kyphosis?.J Clin Med2025;14:5469 PMCID:PMC12347835

[92] Wilson JP Jr,Stewart C,Guthikonda B.Image-guided navigation in spine surgery: from historical developments to future perspectives.J Clin Med2024;13:2036 PMCID:PMC11012660

[93] Gebhard FT,Schneider E,Kinzl L.Does computer-assisted spine surgery reduce intraoperative radiation doses?.Spine2006;31:2024-7; discussion 2028

[94] Amiot LP,Putzier M,Labelle H.Comparative results between conventional and computer-assisted pedicle screw installation in the thoracic, lumbar, and sacral spine.Spine2000;25:606-14

[95] Verma R,Haendlmayer K.Functional outcome of computer-assisted spinal pedicle screw placement: a systematic review and meta-analysis of 23 studies including 5,992 pedicle screws.Eur Spine J2010;19:370-5 PMCID:PMC2899753

[96] Liebmann F,Stütz D.Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery.Med Image Anal2024;91:103027

[97] Abumoussa A,Succop B.Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph.Neurosurg Focus2023;54:E16

[98] Doerr SA,Huang Y.Data-driven detection and registration of spine surgery instrumentation in intraoperative images.Proc SPIE Int Soc Opt Eng2020;11315:113152P PMCID:PMC9450103

[99] Burström G,Hoppenbrouwers J.Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography.J Neurosurg Spine2019;31:147-54

[100] Jecklin S,Farshad M,Esfandiari H.X23D-intraoperative 3D lumbar spine shape reconstruction based on sparse multi-view X-ray data.J Imaging2022;8:271 PMCID:PMC9604813

[101] Luchmann D,Cavalcanti NA.Spinal navigation with AI-driven 3D-reconstruction of fluoroscopy images: an ex-vivo feasibility study.BMC Musculoskelet Disord2024;25:925 PMCID:PMC11575073

[102] Richards PJ,Metelko M.Spine computed tomography doses and cancer induction.Spine2010;35:430-3

[103] van Stralen M,Zijlstra F. BoneMRI of the cervical spine: deep learning-based radiodensity contrast generation for selective visualization of osseous structures. In: ISMRM 27^th Annual Meeting and Exhibition; 2019 May 11-16; Montréal, Canada. Hoboken: John Wiley & Sons, Inc.; 2019. Available from https://ismrm.gitlab.io/2019/1142.html [accessed 16 April 2026].

[104] van der Kolk BYMM,Nijholt IM.Bone visualization of the cervical spine with deep learning-based synthetic CT compared to conventional CT: a single-center noninferiority study on image quality.Eur J Radiol2022;154:110414

[105] Staartjes VE,Vandertop WP,Schröder ML.Magnetic resonance imaging-based synthetic computed tomography of the lumbar spine for surgical planning: a clinical proof-of-concept.Neurosurg Focus2021;50:E13

[106] Davidar AD,Hersh AM.Robot-assisted screw fixation in a cadaver utilizing magnetic resonance imaging-based synthetic computed tomography: toward radiation-free spine surgery. Illustrative case.J Neurosurg Case Lessons2023;6:CASE23120 PMCID:PMC10555644

[107] Azad TD,Tracz JA,Judy BF.Augmented reality in spine surgery - past, present, and future.Spine J2024;24:1-13

[108] Ghaednia H,Lans A.Augmented and virtual reality in spine surgery, current applications and future potentials.Spine J2021;21:1617-25

[109] Pierzchajlo N,Huynh H.Augmented reality in minimally invasive spinal surgery: a narrative review of available technology.World Neurosurg2023;176:35-42

[110] Elmi-Terander A,Nachabe R.Pedicle screw placement using augmented reality surgical navigation with intraoperative 3D imaging: a first in-human prospective cohort study.Spine2019;44:517-25 PMCID:PMC6426349

[111] Butler AJ,Lynch J.Augmented reality in minimally invasive spine surgery: early efficiency and complications of percutaneous pedicle screw instrumentation.Spine J2023;23:27-33

[112] Ma Y,Dong Y,Ma X.Augmented reality navigation system enhances the accuracy of spinal surgery pedicle screw placement: a randomized, multicenter, parallel-controlled clinical trial.Orthop Surg2025;17:631-43 PMCID:PMC11787979

[113] Auloge P,Ramamurthy N.Augmented reality and artificial intelligence-based navigation during percutaneous vertebroplasty: a pilot randomised clinical trial.Eur Spine J2020;29:1580-9

[114] Carl B,Saß B.Microscope-based augmented reality in degenerative spine surgery: initial experience.World Neurosurg2019;128:e541-51

[115] McCloskey K,Ahmad HS,Chauhan D.Virtual and augmented reality in spine surgery: a systematic review.World Neurosurg2023;173:96-107

[116] Cui P,Xu J.Tissue recognition in spinal endoscopic surgery using deep learning. In: 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST); 2019 Oct 23-25; Morioka, Japan. New York: IEEE; 2019. pp 1-5.

[117] Cui P,Lei J.Nerve recognition in percutaneous transforaminal endoscopic discectomy using convolutional neural network.Med Phys2021;48:2279-88

[118] Peng S,Ye Y,Chang Y. Spinal nerve segmentation method and dataset construction in endoscopic surgical scenarios. arXiv 2023;arXiv:2307.10955. Available from: http://arxiv.org/abs/2307.10955 [accessed 16 April 2026].

[119] ATEC launches EOS insight, an end-to-end spine surgery platform powered by EOS imaging and AI. ATEC (Carlsbad, CA, USA), July 24, 2024. Available from https://investors.alphatecspine.com/press-releases/news-details/2024/ATEC-Launches-EOS-InsightTM-an-End-To-End-Spine-Surgery-Platform-Powered-by-EOS-Imaging-and-AI/default.aspx [accessed 16 April 2026].

[120] Carlsmed. Technology Platform. Available from https://carlsmed.com/aprevo-technology-platform/ [accessed 16 April 2026].

[121] Fiere V,Burger E. UNID Patient-Specific Rods show a reduction in rod breakage incidence. New York: Medicrea USA Corp. 2017. Available from https://api.semanticscholar.org/CorpusID:13695587 [accessed 16 April 2026].

[122] Fernandes RJR,Kanawati AJ.Biomechanical comparison of subsidence between patient-specific and non-patient-specific lumbar interbody fusion cages.Global Spine J2024;14:1155-63 PMCID:PMC11289543

[123] Sadrameli SS,Asghar J.Predictability in achieving target intervertebral lordosis using personalized interbody implants.Int J Spine Surg2024;18:S16-23 PMCID:PMC11483450

[124] Proprio - Technology. Available from https://www.propriovision.com/technology [accessed 16 April 2026].

[125] Medivis receives FDA 510(k) clearance for spine navigation and announces commercial launch in the United States. Medivis (New York, NY, USA), April 02, 2025. Available from https://www.prnewswire.com/news-releases/medivis-receives-fda-510k-clearance-for-spine-navigation-and-announces-commercial-launch-in-the-united-states-302418834.html [accessed 16 April 2026].

[126] Molina CA,Ahmed AK.Augmented reality-assisted pedicle screw insertion: a cadaveric proof-of-concept study.J Neurosurg Spine2019;31:139-46

[127] Deng Y,Hui Y. CTSpine1K: a large-scale dataset for spinal vertebrae segmentation in computed tomography. arXiv 2021;arXiv:2105.14711. Available from http://arxiv.org/abs/2105.14711 [accessed 16 April 2026].

[128] Medtronic. UNiD^TM adaptive spine intelligence. Available from: https://www.medtronic.com/en-us/healthcare-professionals/products/spinal-orthopedic/complex-spine/fixation-systems/unid-adaptive-spine-intelligence.html. [Last accessed on 16 Apr 2026]

[129] Mumuni A.Data augmentation: a comprehensive survey of modern approaches.Array2022;16:100258

[130] Miladinović A,Ajčević M.Evaluating deep learning models for classifying OCT images with limited data and noisy labels.Sci Rep2024;14:30321 PMCID:PMC11621707

[131] Basha NK,Muthukumaran K,Mittal V.Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation.Sci Rep2024;14:17615 PMCID:PMC11289405

[132] Seyyed-Kalantari L,McDermott MBA,Ghassemi M.Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations.Nat Med2021;27:2176-82 PMCID:PMC8674135

[133] Loftus TJ,Balch JA.Artificial intelligence-enabled decision support in surgery: state-of-the-art and future directions.Ann Surg2023;278:51-8 PMCID:PMC12292512

[134] Stanley EAM,Wilms M.Where, why, and how is bias learned in medical image analysis models?.EBioMedicine2025;111:105501 PMCID:PMC11700256

[135] Wessels L,Bohner G,Hecht N.Spinal alignment shift between supine and prone CT imaging occurs frequently and regardless of the anatomic region, risk factors, or pathology.Neurosurg Rev2022;45:855-63 PMCID:PMC8827393

[136] Wang F,Li J.Surgical smoke removal via residual Swin transformer network.Int J Comput Assist Radiol Surg2023;18:1417-27

[137] Tejani AS,Hussain M,O’Donnell KP.Integrating and adopting AI in the radiology workflow: a primer for standards and Integrating the Healthcare Enterprise (IHE) profiles.Radiology2024;311:e232653 PMCID:PMC11208735

[138] Hasani N,Rhamim A.Trustworthy artificial intelligence in medical imaging.PET Clin2022;17:1-12 PMCID:PMC8785402

[139] Xia P,Tian J,Hou R. CARES: a comprehensive benchmark of trustworthiness in medical vision language models. arXiv 2024;arXiv:2406.06007. Available from http://arxiv.org/abs/2406.06007 [accessed 16 April 2026].

[140] Petrick N,Delfino JG.Regulatory considerations for medical imaging AI/ML devices in the United States: concepts and challenges.J Med Imaging2023;10:051804 PMCID:PMC10289177

[141] Warraich HJ,Califf RM.FDA perspective on the regulation of artificial intelligence in health care and biomedicine.JAMA2025;333:241-7

[142] Partisan Bill. Georgia House Bill 887. LegiScan. Available from: https://legiscan.com/GA/text/HB887/id/2877485. [Last accessed on 16 Apr 2026]

[143] Gonzalez K. Senate Bill S7896. The New York State Senate. Available from: https://www.nysenate.gov/legislation/bills/2025/S7896. [Last accessed on 16 Apr 2026]

[144] Schweikert D. Healthy Technology Act of 2025. Available from: https://www.congress.gov/bill/119th-congress/house-bill/238. [Last accessed on 16 Apr 2026]

[145] Parikh RB.Paying for artificial intelligence in medicine.NPJ Digit Med2022;5:63 PMCID:PMC9123184

[146] Chen MM,Nicola GN.Who will pay for AI?.Radiol Artif Intell2021;3:e210030 PMCID:PMC8166111

[147] Dogra S,Rajpurkar P.Reimbursement in the age of generalist radiology artificial intelligence.NPJ Digit Med2024;7:350 PMCID:PMC11612271

[148] Brin D.Cost-effectiveness of artificial intelligence tools in radiology: a systematic review.Eur Radiol2025;

[149] Hiligsmann M,Reginster JY.Cost-effectiveness of opportunistic osteoporosis screening using chest radiographs with deep learning in the United States.J Am Coll Radiol2025;22:1346-56