Has artificial intelligence in spine surgery lived up to the hype? A narrative review of recent approaches, current challenges, and the path forward

Abstract

Healthcare applications of artificial intelligence (AI) and machine learning (ML) are currently in a stage of exponential growth; however, their adoption into clinical practice across clinical specialties remains uneven. In spine surgery, the presence of challenging clinical problems, advanced intraoperative technologies, and large multi-center datasets positions the field well for the integration of these technologies into the clinic and operating room (OR). Here, we review recent advances in AI/ML applications in several key domains of spine surgery, identify methodological challenges shared by many approaches, and suggest solutions that may lead to these approaches becoming validated, commercialized tools that can reach clinical practice. Ultimately, we aim for this narrative review to help catalyze further progress in the development and commercialization of AI/ML to benefit future spine patients.

Keywords

Artificial intelligence / machine learning / spine surgery

References

[1] Cornwall GB,Walsh WR,Vaccaro A.Innovation and new technologies in spine surgery, circa 2020: a fifty-year review.Front Surg2020;7:575318 PMCID:PMC7732641

[2] Kazemi N,Tredway TL.The future of spine surgery: new horizons in the treatment of spinal disorders.Surg Neurol Int2013;4:S15-21 PMCID:PMC3642747

[3] Walker CT,Chang SW.History and advances in spinal neurosurgery.J Neurosurg Spine2019;31:775-85

[4] Zahlan A,Hayes D.Artificial intelligence innovation in healthcare: literature review, exploratory analysis, and future research.Technol Soc2023;74:102321

[5] Zhou S,Sun Y.The application of artificial intelligence in spine surgery.Front Surg2022;9:885599 PMCID:PMC9403075

[6] Suran M.New NIH Program for artificial intelligence in research.JAMA2022;328:1580

[7] Clark P,Aphinyanaphongs Y.Marketing and US Food and Drug Administration clearance of artificial intelligence and machine learning enabled software in and as medical devices: a systematic review.JAMA Netw Open2023;6:e2321792 PMCID:PMC10323702

[8] McNabb NK,Rula EY.Projected growth in FDA-approved artificial intelligence products given venture capital funding.J Am Coll Radiol2024;21:617-23

[9] Hoy D,Brooks P.The global burden of low back pain: estimates from the Global Burden of Disease 2010 study.Ann Rheum Dis2014;73:968-74

[10] Stewart Williams J,Peltzer K.risk factors and disability associated with low back pain in older adults in low- and middle-income countries. Results from the WHO Study on Global AGEing and Adult Health (SAGE).PLoS One2015;10:e0127880 PMCID:PMC4456393

[11] Missios S.Hospitalization cost after spine surgery in the United States of America.J Clin Neurosci2015;22:1632-7

[12] Parker SL,Devin CJ.Bending the cost curve-establishing value in spine surgery.Neurosurgery2017;80:S61-9

[13] Jiang F,Badhiwala JH.Quality and safety improvement in spine surgery.Global Spine J2020;10:17S-28S PMCID:PMC6947676

[14] Walid MS.Economic impact of comorbidities in spine surgery.J Neurosurg Spine2011;14:318-21

[15] Dangeti P. Statistics for machine learning. Packt Publishing Ltd; 2017. Available from: https://books.google.com/books?id=C-dDDwAAQBAJ&printsec=frontcover&hl=zh-CN#v=onepage&q&f=false. [Last accessed on 28 Dec 2024]

[16] Goodfellow I,Courville A. Deep Learning. MIT Press; 2016. Available from: https://www.deeplearningbook.org/. [Last accessed on 28 Dec 2024]

[17] Chan AK,Bisson EF.Classifying patients operated for spondylolisthesis: a K-means clustering analysis of clinical presentation phenotypes.Neurosurgery2021;89:1033-41

[18] Ames CP, Smith JS, Pellisé F, et al; European Spine Study Group, International Spine Study Group. Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value. Spine 2019;44:915-26.

[19] Ames CP, Smith JS, Pellisé F, et al; European Spine Study Group, International Spine Study Group. Development of predictive models for all individual questions of SRS-22R after adult spinal deformity surgery: a step toward individualized medicine. Eur Spine J 2019;28:1998-2011.

[20] Gros C,Badji A.Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.Neuroimage2019;184:901-15 PMCID:PMC6759925

[21] Jamaludin A, Lootus M, Kadir T, et al; Genodisc Consortium. 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. Eur Spine J 2017;26:1374-83.

[22] De Leener B, Cohen-Adad J, Kadoury S. Automatic segmentation of the spinal cord and spinal canal coupled with vertebral labeling.IEEE Trans Med Imaging2015;34:1705-18

[23] McCoy DB, Dupont SM, Gros C, et al; TRACK-SCI Investigators. 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. AJNR Am J Neuroradiol 2019;40:737-44. PMCID:PMC7048524

[24] Doerr SA,Hersh AM.Automated prediction of the thoracolumbar injury classification and severity score from CT using a novel deep learning algorithm.Neurosurg Focus2022;52:E5

[25] Pang S,Su Z.DGMSNet: spine segmentation for MR image by a detection-guided mixed-supervised segmentation network.Med Image Anal2022;75:102261

[26] Pang S,Zhao L.SpineParseNet: spine parsing for volumetric MR image by a two-stage segmentation framework with semantic image representation.IEEE Trans Med Imaging2021;40:262-73

[27] Wesselink EO,Coppieters MW.Convolutional neural networks for the automatic segmentation of lumbar paraspinal muscles in people with low back pain.Sci Rep2022;12:13485 PMCID:PMC9355981

[28] Zhang B,Ning Z.Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: a multicenter retrospective cohort study.Bone2020;140:115561

[29] Yabu A,Tabuchi H.Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images.Spine J2021;21:1652-8

[30] Jardon M,Chazen JL.Deep-learning-reconstructed high-resolution 3D cervical spine MRI for foraminal stenosis evaluation.Skeletal Radiol2023;52:725-32

[31] Trinh GM,Hsieh KL.Detection of lumbar spondylolisthesis from X-ray images using deep learning network.J Clin Med2022;11:5450 PMCID:PMC9501139

[32] Grob A,Jamaludin A.External validation of the deep learning system “SpineNet” for grading radiological features of degeneration on MRIs of the lumbar spine.Eur Spine J2022;31:2137-48

[33] Berlin C,Grover P.Novel AI-based algorithm for the automated computation of coronal parameters in adolescent idiopathic scoliosis patients: a validation study on 100 preoperative full spine X-rays.Global Spine J2024;14:1728-37 PMCID:PMC11268300

[34] Wu H,Rasoulinejad P.Automated comprehensive adolescent idiopathic scoliosis assessment using MVC-Net.Med Image Anal2018;48:1-11

[35] Weng CH,Huang YJ.Artificial intelligence for automatic measurement of sagittal vertical axis using ResUNet framework.J Clin Med2019;8:1826 PMCID:PMC6912675

[36] Korez R,Vrtovec T.A deep learning tool for fully automated measurements of sagittal spinopelvic balance from X-ray images: performance evaluation.Eur Spine J2020;29:2295-305

[37] Galbusera F,Wilke HJ.Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach.Eur Spine J2019;28:951-60

[38] Berven S,Glassman S. Optimizing surgical alignment: intraoperative assessment of alignment using zero radiation, volumetric intelligence. 2023. Available from: https://assets-global.website-files.com/6296c202ecfe835c15f4e757/649d0b69ba75cbd927e0717b_PROPRIO_WhitePaperLayout_R2.pdf. [Last accessed on 28 Dec 2024]

[39] Abdelrahman A,Bala A.Percutaneous posterior lumbar interbody fusion using optical topographic navigation: operative technique.Interdiscip Neurosur2022;29:101561

[40] Comstock CP.Novel machine vision image guidance system significantly reduces procedural time and radiation exposure compared with 2-dimensional fluoroscopy-based guidance in pediatric deformity surgery.J Pediatr Orthop2023;43:e331-6 PMCID:PMC10082057

[41] Lim KBL,Ng SWL,Lee NKL.The machine-vision image guided surgery system reduces fluoroscopy time, ionizing radiation and intraoperative blood loss in posterior spinal fusion for scoliosis.Eur Spine J2023;32:3987-95

[42] Yeretsian T,Guha D,Yang VXD.Machine-vision image guided C4-C5 unilateral cervical pedicle screw insertion: case report and review of literature.AME Case Rep2022;6:9 PMCID:PMC8762388

[43] Malacon K,Zygourakis CC.First reported use of machine vision image guided system for unstable thoracolumbar fusion: technical case report.Interdiscip Neurosur2022;30:101641

[44] Eliahu K,Wang MY.Applications for augmented and virtual reality in robot-assisted spine surgery.Curr Robot Rep2022;3:33-7

[45] 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

[46] 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

[47] Elmi-Terander A,Nachabé R.Augmented reality navigation with intraoperative 3D imaging vs fluoroscopy-assisted free-hand surgery for spine fixation surgery: a matched-control study comparing accuracy.Sci Rep2020;10:707 PMCID:PMC6971085

[48] Charles YP,Nachabe R,Steib JP.Minimally invasive transforaminal lumbar interbody fusion using augmented reality surgical navigation for percutaneous pedicle screw placement.Clin Spine Surg2021;34:E415-24

[49] Zhang Y,Chen M.Automated machine learning-based model for the prediction of delirium in patients after surgery for degenerative spinal disease.CNS Neurosci Ther2023;29:282-95 PMCID:PMC9804056

[50] Khazanchi R,Shah RM.Using machine learning and deep learning algorithms to predict postoperative outcomes following anterior cervical discectomy and fusion.Clin Spine Surg2023;36:143-9

[51] Karhade AV,Groot OQ.Development of machine learning and natural language processing algorithms for preoperative prediction and automated identification of intraoperative vascular injury in anterior lumbar spine surgery.Spine J2021;21:1635-42

[52] Karabacak M.A machine learning-based online prediction tool for predicting short-term postoperative outcomes following spinal tumor resections.Cancers2023;15:812 PMCID:PMC9913622

[53] Han SS,Suarez PA.A machine learning approach for predictive models of adverse events following spine surgery.Spine J2019;19:1772-81

[54] Park C,Gottfried ON.Which supervised machine learning algorithm can best predict achievement of minimum clinically important difference in neck pain after surgery in patients with cervical myelopathy? A QOD study.Neurosurg Focus2023;54:E5

[55] Khan O,Witiw CD,Fehlings MG.Machine learning algorithms for prediction of health-related quality-of-life after surgery for mild degenerative cervical myelopathy.Spine J2021;21:1659-69

[56] Rushton AB,Verra ML.Predictors of poor outcome following lumbar spinal fusion surgery: a prospective observational study to derive two clinical prediction rules using British Spine Registry data.Eur Spine J2023;32:2303-18

[57] Müller D,Fekete TF.Development of a machine-learning based model for predicting multidimensional outcome after surgery for degenerative disorders of the spine.Eur Spine J2022;31:2125-36

[58] 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

[59] Fiani B,Kondilis A,Reier L.Virtual reality in neurosurgery: “Can you see it?.World Neurosurg2020;141:291-8

[60] Goldberg JL,Elowitz E.Challenges hindering widespread adoption of minimally invasive spinal surgery.World Neurosurg2022;163:228-32

[61] Ghanem M,El-Hajj VG.Limitations in evaluating machine learning models for imbalanced binary outcome classification in spine surgery: a systematic review.Brain Sci2023;13:1723 PMCID:PMC10741524

[62] Brown TB,Ryder N.Language models are few-shot learners.arXiv2020;arXiv:2005.14165Available from: https://doi.org/10.48550/arXiv.2005.14165. [accessed 28 Dec 2024]