Page 117 - Read Online
P. 117
Ambati et al. Art Int Surg. 2025;5:53-64 https://dx.doi.org/10.20517/ais.2024.45 Page 63
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. DOI PubMed PMC
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. DOI
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. DOI
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. DOI
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. DOI PubMed PMC
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. DOI
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. DOI
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. DOI
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. DOI PubMed PMC
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. DOI PubMed
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. DOI PubMed
PMC
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. DOI PubMed
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. DOI PubMed PMC
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. DOI PubMed
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. DOI
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. DOI
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. DOI
PubMed PMC
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. DOI PubMed
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. DOI PubMed PMC
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. DOI
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. DOI
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. DOI
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.
DOI
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. DOI PubMed PMC
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. DOI
PubMed
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. DOI PubMed PMC