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Abstract
Artificial intelligence (AI) has been widely applied in spinal surgery, contributing significantly to clinical disease diagnosis, surgical treatment decision-making, prognosis prediction, intraoperative intelligent navigation, surgical rehabilitation, and the advancement of surgical instruments. Nevertheless, current research predominantly focuses on evaluating model performance, often neglecting clear indicators of clinical utility. Moreover, several challenges persist, including low-quality datasets, heterogeneity in research reports, insufficient algorithm transparency, and limited clinical application scenarios. Looking ahead, by enhancing the reliability and clinical efficacy of algorithms from multiple perspectives, AI is expected to enable comprehensive management of spinal surgical diseases throughout the preoperative, intraoperative, and postoperative phases.
Keywords
artificial intelligence
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diagnosis
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prognosis
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spine
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surgical treatment
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Chenfei Gao, Tianyu Yao, Tenghui Zhang, Wenyu Zhang, Jianxi Wang, Fazhi Zang, Huajiang Chen.
Empowering the future of spinal surgery through digital and intelligent technologies.
Spine Research, 2025, 1(1): 23-30 DOI:10.1097/br9.0000000000000003
| [1] |
Yu K, Healey E, Leong T, Kohane IS, Manrai AK. Medical artificial intelligence and human values. N Engl J Med. 2024;390:1895–904.
|
| [2] |
Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349:255–60.
|
| [3] |
Toma A, Diller G, Lawler PR. Deep learning in medicine. JACC Adv. 2022;1:100017.
|
| [4] |
Ren G, Yu K, Xie Z, et al. Current applications of machine learning in spine: from clinical view. Global Spine J. 2022;12:1827–40.
|
| [5] |
Lopez CD, Boddapati V, Lombardi JM, et al. Artificial learning and machine learning applications in spine surgery: a systematic review. Global Spine J. 2022;12:1561–72.
|
| [6] |
Hornung AL, Hornung CM, Mallow GM, et al. Artificial intelligence in spine care: current applications and future utility. Eur Spine J. 2022;31:2057–81.
|
| [7] |
Hornung AL, Hornung CM, Mallow GM, et al. Artificial intelligence and spine imaging: limitations, regulatory issues and future direction. Eur Spine J. 2022;31:2007–21.
|
| [8] |
Galbusera F, Casaroli G, Bassani T. Artificial intelligence and machine learning in spine research. JOR Spine. 2019;2:e1044.
|
| [9] |
Da Mutten R, Zanier O, Theiler S, et al. Whole spine segmentation using object detection and semantic segmentation. Neurospine. 2024;21:57–67.
|
| [10] |
Zou L, Guo L, Zhang R, et al. VLTENet: a deep-learning-based vertebra localization and tilt estimation network for automatic Cobb angle estimation. IEEE J Biomed Health Inf. 2023;27:3002–13.
|
| [11] |
Mohanty R, Allabun S, Solanki SS, et al. NAMSTCD: a novel augmented model for spinal cord segmentation and tumor classification using deep nets. Diagnostics (Basel, Switzerland). 2023;13:1417.
|
| [12] |
Meng N, Cheung JPY, Wong KK, et al. An artificial intelligence powered platform for auto-analyses of spine alignment irrespective of image quality with prospective validation. eClinicalMedicine. 2022;43:101252.
|
| [13] |
Zhang T, Zhu C, Zhao Y, et al. Deep learning model to classify and monitor idiopathic scoliosis in adolescents using a single smartphone photograph. JAMA Netw Open. 2023;6:e2330617–e2330617.
|
| [14] |
Gitto S, Bologna M, Corino VDA, et al. Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance. Radiol Med. 2022;127:518–25.
|
| [15] |
Voter AF, Larson ME, Garrett JW, Yu JPJ. Diagnostic accuracy and failure mode analysis of a deep learning algorithm for the detection of cervical spine fractures. Am J Neuroradiol. 2021;42:1550–6.
|
| [16] |
Staartjes VE, Klukowska AM, Vieli M, et al. Machine learning–augmented objective functional testing in the degenerative spine: quantifying impairment using patient-specific five-repetition sit-to-stand assessment. Neurosurg Focus. 2021;51:E8.
|
| [17] |
Broida SE, Schrum ML, Yoon E, et al. Improving surgical triage in spine clinic: predicting likelihood of surgery using machine learning. World Neurosurg. 2022;163:e192–8.
|
| [18] |
Baroncini A, Campagner A, Cabitza F, et al. The use of machine learning for the prediction of response to follow-up in spine registries. Int J Med Inform. 2025;195:105752.
|
| [19] |
Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med. 2023;29:1930–40.
|
| [20] |
Karhade AV, Bongers MER, Groot OQ, et al. 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 J. 2021;21:1635–42.
|
| [21] |
Karhade AV, Lavoie-Gagne O, Agaronnik N, et al. Natural language processing for prediction of readmission in posterior lumbar fusion patients: which free-text notes have the most utility? Spine J. 2022;22:272–7.
|
| [22] |
Alanazi AH, Cradock A, Rainford L. Development of lumbar spine MRI referrals vetting models using machine learning and deep learning algorithms: comparison models vs healthcare professionals. Radiography (Lond). 2022;28:674–83.
|
| [23] |
Pedro KM, Alvi MA, Hejrati N, Quddusi AI, Singh A, Fehlings MG. Machine learning-based cluster analysis iden-tifies four unique phenotypes of patients with degenerative cervical myelopathy with distinct clinical profiles and long-term functional and neurological outcomes. Ebiomedicine. 2024;106:105226.
|
| [24] |
Sarikonda A, Isch E, Self M, et al. Evaluating the adherence of large language models to surgical guidelines: a comparative analysis of Chatbot recommendations and North American Spine Society (NASS) coverage criteria. Cureus. 2024;16:e68521.
|
| [25] |
Zhou S, Zhou F, Sun Y, et al. The application of artificial intelligence in spine surgery. Front Surg. 2022;9:885599.
|
| [26] |
Burström G, Buerger C, Hoppenbrouwers J, et al. 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 Spine. 2019;31:147–54.
|
| [27] |
Kalfas IH. Machine vision navigation in spine surgery. Front Surg. 2021;8:640554.
|
| [28] |
Jakubovic R, Guha D, Gupta S, et al. High speed, high density intraoperative 3D optical topographical imaging with efficient registration to MRI and CT for craniospinal surgical navigation. Sci Rep. 2018;8:14894.
|
| [29] |
Jecklin S, Jancik C, Farshad M, Fürnstahl P, Esfandiari H. X23D—intraoperative 3D lumbar spine shape reconstruction based on sparse multi-view X-ray data. J Imaging. 2022;8:271.
|
| [30] |
Edström E, Burström G, Nachabe R, Gerdhem P, Elmi Terander A. A novel augmented-reality-based surgical navigation system for spine surgery in a hybrid operating room: design, workflow, and clinical applications. Oper Neurosurg. 2020;18:496–502.
|
| [31] |
Edström E, Burström G, Omar A, et al. Augmented reality surgical navigation in spine surgery to minimize staff radiation exposure. Spine. 2020;45:E45–53.
|
| [32] |
Auloge P, Cazzato RL, Ramamurthy N, et al. Augmented reality and artificial intelligence-based navigation during percutaneous vertebroplasty: a pilot randomised clinical trial. Eur Spine J. 2020;29:1580–9.
|
| [33] |
Liu S, Yang J, Jin H, et al. Exploration of the application of augmented reality technology for teaching spinal tumor’s anatomy and surgical techniques. Front Med. 2024;11:1403423.
|
| [34] |
Hasan S, Miller A, Higginbotham D, Saleh ES, McCarty S. Virtual and augmented reality in spine surgery: an era of immersive healthcare. Cureus. 2023;15:e43964.
|
| [35] |
Li Z, Wang C, Song X, et al. Accuracy evaluation of a novel spinal robotic system for autonomous laminectomy in thoracic and lumbar vertebrae: a cadaveric study. J Bone Joint Surg Am. 2023;105:943–50.
|
| [36] |
Cheng L, Liu J, Lian L, et al. Predicting deep surgical site infection in patients receiving open posterior instrumented thoracolumbar surgery: A-DOUBLE-SSI risk score – a large retrospective multicenter cohort study in China. Int J Surg. 2023;109:2276–85.
|
| [37] |
Yasheng P, Yusufu A, Yimiti Y, Luan H, Peng C, Song X. Web-based machine learning application for interpretable prediction of prolonged length of stay after lumbar spinal stenosis surgery: a retrospective cohort study with explainable AI. Front Physiol. 2025;16:1542240.
|
| [38] |
Rodrigues AJ, Schonfeld E, Varshneya K, et al. Comparison of deep learning and classical machine learning algorithms to predict postoperative outcomes for anterior cervical discec-tomy and fusion procedures with state-of-the-art performance. Spine. 2022;47:1637–44.
|
| [39] |
Merali ZG, Witiw CD, Badhiwala JH, Wilson JR, Fehlings MG. Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy. PLoS One. 2019;14:e0215133.
|
| [40] |
Gabriel RA, Park BH, Hsu C, Macias AA. A review of leveraging artificial intelligence to predict persistent postoperative opioid use and opioid use disorder and its ethical consider-ations. Curr Pain Headache Rep. 2025;29:30.
|
| [41] |
Karhade AV, Ogink PT, Thio QCBS, et al. Development of machine learning algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation. Spine J. 2019;19:1764–71.
|
| [42] |
Yagi M, Michikawa T, Yamamoto T, et al; Keio Spine Research Group. Development and validation of machine learning-based predictive model for clinical outcome of decompression surgery for lumbar spinal canal stenosis. Spine J. 2022;22:1768–77.
|
| [43] |
Ames CP, Smith JS, Pellisé F, et al; European 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.
|
| [44] |
Khan O, Badhiwala JH, Witiw CD, Wilson JR, Fehlings MG. Machine learning algorithms for prediction of health-related quality-of-life after surgery for mild degenerative cervical myelopathy. Spine J. 2021;21:1659–69.
|
| [45] |
Chakravorty A, Mobbs RJ, Anderson DB, et al. The role of wearable devices and objective gait analysis for the assessment and monitoring of patients with lumbar spinal stenosis: sys-tematic review. BMC Musculoskelet Disord. 2019;20:288.
|
| [46] |
Lee SI, Campion A, Huang A, et al. Identifying predictors for postoperative clinical outcome in lumbar spinal stenosis patients using smart-shoe technology. J Neuroeng Rehabil. 2017;14:77.
|
| [47] |
Sun J, Liu Y, Yan S, et al. Clinical gait evaluation of patients with lumbar spine stenosis. Orthop Surg. 2018;10:32–9.
|
| [48] |
Mongan J, Moy L, Kahn CE, Jr. Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell. 2020;2:e200029.
|
| [49] |
Tejani AS, Klontzas ME, Gatti AA, et al; CLAIM 2024 Update Panel. Checklist for artificial intelligence in medical imaging (CLAIM): 2024 Update. Radiol Artif Intell. 2024;6:e240300.
|
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