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REFERENCES
1. How NE, Street JT, Dvorak MF, et al. Pseudarthrosis in adult and pediatric spinal deformity surgery: a systematic review of the
literature and meta-analysis of incidence, characteristics, and risk factors. Neurosurg Rev 2019;42:319-36. DOI PubMed
2. Deyo RA, Mirza SK, Martin BI, Kreuter W, Goodman DC, Jarvik JG. Trends, major medical complications, and charges associated
with surgery for lumbar spinal stenosis in older adults. JAMA 2010;303:1259-65. DOI PubMed PMC
3. Buchholz AL, Quinn JC, Shaffrey CI. Postoperative spinal deformities: kyphosis, nonunion, and loss of motion segment. In:
Complications in neurosurgery. 2019. pp. 325-30. Available from: http://103.203.175.90:81/fdScript/RootOfEBooks/E%20Book%
20Collection%202021%20-%20A/ENGLISH/Complications%20in%20Neurosurgery.pdf. [Last accessed on 7 Nov 2024].
4. Kim YJ, Bridwell KH, Lenke LG, Rhim S, Cheh G. Pseudarthrosis in long adult spinal deformity instrumentation and fusion to the
sacrum: prevalence and risk factor analysis of 144 cases. Spine 2006;31:2329-36. DOI PubMed
5. Kim YJ, Bridwell KH, Lenke LG, Rinella AS, Edwards C 2nd. Pseudarthrosis in primary fusions for adult idiopathic scoliosis:
incidence, risk factors, and outcome analysis. Spine 2005;30:468-74. DOI PubMed
6. Kim YJ, Bridwell KH, Lenke LG, Cho KJ, Edwards CC 2nd, Rinella AS. Pseudarthrosis in adult spinal deformity following
multisegmental instrumentation and arthrodesis. J Bone Joint Surg Am 2006;88:721-8. DOI PubMed
7. Pateder DB, Park YS, Kebaish KM, et al. Spinal fusion after revision surgery for pseudarthrosis in adult scoliosis. Spine
2006;31:E314-9. DOI PubMed
8. Joshi RS, Haddad AF, Lau D, Ames CP. Artificial intelligence for adult spinal deformity. Neurospine 2019;16:686-94. DOI PubMed
PMC
9. Joshi RS, Lau D, Ames CP. Artificial intelligence for adult spinal deformity: current state and future directions. Spine J 2021;21:1626-
34. DOI PubMed
10. Lenke LG. Commentary: artificial intelligence for adult spinal deformity. Neurospine 2019;16:695-6. DOI PubMed PMC
11. Perez-Breva L, Shin JH. Artificial intelligence in neurosurgery: a comment on the possibilities. Neurospine 2019;16:640-2. DOI
PubMed PMC
12. Durand WM, Lafage R, Hamilton DK, et al; International Spine Study Group (ISSG). Artificial intelligence clustering of adult spinal
deformity sagittal plane morphology predicts surgical characteristics, alignment, and outcomes. Eur Spine J 2021;30:2157-66. DOI
PubMed
13. Durand WM, Daniels AH, Hamilton DK, et al; International Spine Study Group. Artificial intelligence models predict operative versus
nonoperative management of patients with adult spinal deformity with 86% accuracy. World Neurosurg 2020;141:e239-53. DOI
PubMed
14. Scheer JK, Smith JS, Schwab F, et al; International Spine Study Group. Development of a preoperative predictive model for major
complications following adult spinal deformity surgery. J Neurosurg Spine 2017;26:736-43. DOI PubMed
15. Scheer JK, Osorio JA, Smith JS, et al; International Spine Study Group. Development of validated computer-based preoperative
predictive model for proximal junction failure (PJF) or clinically significant PJK with 86% accuracy based on 510 ASD patients with
2-year follow-up. Spine 2016;41:E1328-35. DOI PubMed
16. Pellisé F, Serra-Burriel M, Smith JS, et al; International Spine Study Group, European Spine Study Group. Development and
validation of risk stratification models for adult spinal deformity surgery. J Neurosurg Spine 2019;31:587-99. DOI PubMed
17. Scheer JK, Oh T, Smith JS, et al; International Spine Study Group. Development of a validated computer-based preoperative predictive
model for pseudarthrosis with 91% accuracy in 336 adult spinal deformity patients. Neurosurg Focus 2018;45:E11. DOI PubMed
18. 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. DOI PubMed PMC
19. Kernbach JM, Staartjes VE. Foundations of machine learning-based clinical prediction modeling: part II - generalization and
overfitting. In: Staartjes VE, Regli L, Serra C, editors. Machine learning in clinical neuroscience. Springer, Cham; 2022. pp. 15-21.
DOI
20. Youden WJ. Index for rating diagnostic tests. Cancer 1950;3:32-5. DOI
21. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-
based localization. Int J Comput Vis 2020;128:336-59. DOI
22. Adogwa O, Buchowski JM, Lenke LG, et al. Comparison of rod fracture rates in long spinal deformity constructs after transforaminal
versus anterior lumbar interbody fusions: a single-institution analysis. J Neurosurg Spine 2020;32:42-9. DOI PubMed
23. Passias PG, Bortz C, Alas H, et al. Alcoholism as a predictor for pseudarthrosis in primary spine fusion: an analysis of risk factors and
30-day outcomes for 52,402 patients from 2005 to 2013. J Orthop 2019;16:36-40. DOI PubMed PMC
24. Marques MF, Fiere V, Obeid I, et al; Société Française de Chirurgie Rachidienne, SFCR. Pseudarthrosis in adult spine deformity
surgery: risk factors and treatment options. Eur Spine J 2021;30:3225-32. DOI PubMed
25. Kawabata A, Yoshii T, Sakai K, et al. Identification of predictive factors for mechanical complications after adult spinal deformity
surgery: a multi-institutional retrospective study. Spine 2020;45:1185-92. DOI PubMed
26. Kuo CC, Soliman MAR, Aguirre AO, et al. Vertebral bone quality score independently predicts proximal junctional kyphosis and/or