REFERENCES
1. Cornwall GB, Davis A, Walsh WR, Mobbs RJ, Vaccaro A. Innovation and new technologies in spine surgery, circa 2020: a fifty-year review. Front Surg. 2020;7:575318.
2. Kazemi N, Crew LK, Tredway TL. The future of spine surgery: new horizons in the treatment of spinal disorders. Surg Neurol Int. 2013;4:S15-21.
3. Walker CT, Kakarla UK, Chang SW, Sonntag VKH. History and advances in spinal neurosurgery. J Neurosurg Spine. 2019;31:775-85.
4. Zahlan A, Ranjan RP, Hayes D. Artificial intelligence innovation in healthcare: literature review, exploratory analysis, and future research. Technol Soc. 2023;74:102321.
5. Zhou S, Zhou F, Sun Y, et al. The application of artificial intelligence in spine surgery. Front Surg. 2022;9:885599.
7. Clark P, Kim J, 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 Open. 2023;6:e2321792.
8. McNabb NK, Christensen EW, Rula EY, et al. Projected growth in FDA-approved artificial intelligence products given venture capital funding. J Am Coll Radiol. 2024;21:617-23.
9. Hoy D, March L, Brooks P, et al. The global burden of low back pain: estimates from the Global Burden of Disease 2010 study. Ann Rheum Dis. 2014;73:968-74.
10. Stewart Williams J, Ng N, Peltzer K, et al. 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 One. 2015;10:e0127880.
11. Missios S, Bekelis K. Hospitalization cost after spine surgery in the United States of America. J Clin Neurosci. 2015;22:1632-7.
12. Parker SL, Chotai S, Devin CJ, et al. Bending the cost curve-establishing value in spine surgery. Neurosurgery. 2017;80:S61-9.
13. Jiang F, Wilson JRF, Badhiwala JH, et al. Quality and safety improvement in spine surgery. Global Spine J. 2020;10:17S-28S.
14. Walid MS, Robinson JS Jr. Economic impact of comorbidities in spine surgery. J Neurosurg Spine. 2011;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, Bengio Y, Courville A. Deep Learning. MIT Press; 2016. Available from: https://www.deeplearningbook.org/. [Last accessed on 28 Dec 2024].
17. Chan AK, Wozny TA, Bisson EF, et al. Classifying patients operated for spondylolisthesis: a K-means clustering analysis of clinical presentation phenotypes. Neurosurgery. 2021;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, De Leener B, Badji A, et al. Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks. Neuroimage. 2019;184:901-15.
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 Imaging. 2015;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.
24. Doerr SA, Weber-Levine C, Hersh AM, et al. Automated prediction of the thoracolumbar injury classification and severity score from CT using a novel deep learning algorithm. Neurosurg Focus. 2022;52:E5.
25. Pang S, Pang C, Su Z, et al. DGMSNet: spine segmentation for MR image by a detection-guided mixed-supervised segmentation network. Med Image Anal. 2022;75:102261.
26. Pang S, Pang C, Zhao L, et al. SpineParseNet: spine parsing for volumetric MR image by a two-stage segmentation framework with semantic image representation. IEEE Trans Med Imaging. 2021;40:262-73.
27. Wesselink EO, Elliott JM, Coppieters MW, et al. Convolutional neural networks for the automatic segmentation of lumbar paraspinal muscles in people with low back pain. Sci Rep. 2022;12:13485.
28. Zhang B, Yu K, Ning Z, et al. Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: a multicenter retrospective cohort study. Bone. 2020;140:115561.
29. Yabu A, Hoshino M, Tabuchi H, et al. Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images. Spine J. 2021;21:1652-8.
30. Jardon M, Tan ET, Chazen JL, et al. Deep-learning-reconstructed high-resolution 3D cervical spine MRI for foraminal stenosis evaluation. Skeletal Radiol. 2023;52:725-32.
31. Trinh GM, Shao HC, Hsieh KL, et al. Detection of lumbar spondylolisthesis from X-ray images using deep learning network. J Clin Med. 2022;11:5450.
32. Grob A, Loibl M, Jamaludin A, et al. External validation of the deep learning system “SpineNet” for grading radiological features of degeneration on MRIs of the lumbar spine. Eur Spine J. 2022;31:2137-48.
33. Berlin C, Adomeit S, Grover P, et al. 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 J. 2024;14:1728-37.
34. Wu H, Bailey C, Rasoulinejad P, Li S. Automated comprehensive adolescent idiopathic scoliosis assessment using MVC-Net. Med Image Anal. 2018;48:1-11.
35. Weng CH, Wang CL, Huang YJ, et al. Artificial intelligence for automatic measurement of sagittal vertical axis using ResUNet framework. J Clin Med. 2019;8:1826.
36. Korez R, Putzier M, Vrtovec T. A deep learning tool for fully automated measurements of sagittal spinopelvic balance from X-ray images: performance evaluation. Eur Spine J. 2020;29:2295-305.
37. Galbusera F, Niemeyer F, Wilke HJ, et al. Fully automated radiological analysis of spinal disorders and deformities: a deep learning approach. Eur Spine J. 2019;28:951-60.
38. Berven S, Bisson E, Glassman S, et al. 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, Bangash OK, Bala A. Percutaneous posterior lumbar interbody fusion using optical topographic navigation: operative technique. Interdiscip Neurosur. 2022;29:101561.
40. Comstock CP, Wait E. 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 Orthop. 2023;43:e331-6.
41. Lim KBL, Yeo ISX, Ng SWL, Pan WJ, 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 J. 2023;32:3987-95.
42. Yeretsian T, Lai C, Guha D, Ramjist J, Yang VXD. Machine-vision image guided C4-C5 unilateral cervical pedicle screw insertion: case report and review of literature. AME Case Rep. 2022;6:9.
43. Malacon K, Fatemi P, Zygourakis CC. First reported use of machine vision image guided system for unstable thoracolumbar fusion: technical case report. Interdiscip Neurosur. 2022;30:101641.
44. Eliahu K, Liounakos J, Wang MY. Applications for augmented and virtual reality in robot-assisted spine surgery. Curr Robot Rep. 2022;3:33-7.
45. 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.
46. 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.
47. Elmi-Terander A, Burström G, Nachabé R, et al. Augmented reality navigation with intraoperative 3D imaging vs fluoroscopy-assisted free-hand surgery for spine fixation surgery: a matched-control study comparing accuracy. Sci Rep. 2020;10:707.
48. Charles YP, Cazzato RL, Nachabe R, Chatterjea A, Steib JP, Gangi A. Minimally invasive transforaminal lumbar interbody fusion using augmented reality surgical navigation for percutaneous pedicle screw placement. Clin Spine Surg. 2021;34:E415-24.
49. Zhang Y, Wan DH, Chen M, et al. Automated machine learning-based model for the prediction of delirium in patients after surgery for degenerative spinal disease. CNS Neurosci Ther. 2023;29:282-95.
50. Khazanchi R, Bajaj A, Shah RM, et al. Using machine learning and deep learning algorithms to predict postoperative outcomes following anterior cervical discectomy and fusion. Clin Spine Surg. 2023;36:143-9.
51. 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.
52. Karabacak M, Margetis K. A machine learning-based online prediction tool for predicting short-term postoperative outcomes following spinal tumor resections. Cancers. 2023;15:812.
53. Han SS, Azad TD, Suarez PA, Ratliff JK. A machine learning approach for predictive models of adverse events following spine surgery. Spine J. 2019;19:1772-81.
54. Park C, Mummaneni PV, Gottfried ON, et al. 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 Focus. 2023;54:E5.
55. 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.
56. Rushton AB, Jadhakhan F, Verra ML, et al. 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 J. 2023;32:2303-18.
57. Müller D, Haschtmann D, Fekete TF, et al. Development of a machine-learning based model for predicting multidimensional outcome after surgery for degenerative disorders of the spine. Eur Spine J. 2022;31:2125-36.
58. Johnson GW, Chanbour H, Ali MA, et al. Artificial intelligence to preoperatively predict proximal junction kyphosis following adult spinal deformity surgery: soft tissue imaging may be necessary for accurate models. Spine. 2023;48:1688-95.
59. Fiani B, De Stefano F, Kondilis A, Covarrubias C, Reier L, Sarhadi K. Virtual reality in neurosurgery: “Can you see it? World Neurosurg. 2020;141:291-8.
60. Goldberg JL, Härtl R, Elowitz E. Challenges hindering widespread adoption of minimally invasive spinal surgery. World Neurosurg. 2022;163:228-32.
61. Ghanem M, Ghaith AK, El-Hajj VG, et al. Limitations in evaluating machine learning models for imbalanced binary outcome classification in spine surgery: a systematic review. Brain Sci. 2023;13:1723.
62. Brown TB, Mann B, Ryder N, et al. Language models are few-shot learners. arXiv. 2020;arXiv:2005.14165. Available from: https://doi.org/10.48550/arXiv.2005.14165. [accessed 28 Dec 2024]
