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Glaser et al. Art Int Surg. 2025;5:1-15 https://dx.doi.org/10.20517/ais.2024.36 Page 13

DECLARATIONS
Authors’ contributions
Manuscript writing and revision: Glaser D
Data collection, analysis, and manuscript revision: AlMekkawi AK, Caruso JP
Data contribution and manuscript revision: Chung CY, Khan EZ, Daadaa HM
Conceptualization, progress monitoring, and final manuscript revision: Aoun SG, Bagley CA

Availability of data and materials
The data are available from the corresponding author upon reasonable request.

Financial support and sponsorship
None.

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2025.

REFERENCES
1. Le Huec JC, Thompson W, Mohsinaly Y, Barrey C, Faundez A. Sagittal balance of the spine. Eur Spine J. 2019;28:1889-905. DOI
PubMed
2. Vrtovec T, Janssen MM, Likar B, Castelein RM, Viergever MA, Pernuš F. A review of methods for evaluating the quantitative
parameters of sagittal pelvic alignment. Spine J. 2012;12:433-46. DOI PubMed
3. Legaye J, Duval-Beaupère G, Hecquet J, Marty C. Pelvic incidence: a fundamental pelvic parameter for three-dimensional regulation
of spinal sagittal curves. Eur Spine J. 1998;7:99-103. DOI PubMed PMC
4. Glassman SD, Bridwell K, Dimar JR, Horton W, Berven S, Schwab F. The impact of positive sagittal balance in adult spinal
deformity. Spine. 2005;30:2024-9. DOI PubMed
5. Maillot C, Ferrero E, Fort D, Heyberger C, Le Huec JC. Reproducibility and repeatability of a new computerized software for sagittal
spinopelvic and scoliosis curvature radiologic measurements: Keops®. Eur Spine J. 2015;24:1574-81. DOI PubMed
6. Lafage R, Ferrero E, Henry JK, et al. Validation of a new computer-assisted tool to measure spino-pelvic parameters. Spine J.
2015;15:2493-502. DOI PubMed
7. Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88. DOI
8. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221-48. DOI PubMed PMC
9. Galbusera F, Casaroli G, Bassani T. Artificial intelligence and machine learning in spine research. JOR Spine. 2019;2:e1044. DOI
PubMed PMC
10. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-44. DOI PubMed
11. Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29:102-27.
DOI PubMed
12. Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from
medical imaging: a systematic review and meta-analysis. Lancet Digit Health. 2019;1:e271-97. DOI
13. Chea P, Mandell JC. Current applications and future directions of deep learning in musculoskeletal radiology. Skeletal Radiol.
2020;49:183-97. DOI PubMed
14. 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. DOI
15. Lopez CD, Boddapati V, Lombardi JM, et al. Artificial learning and machine learning applications in spine surgery: a systematic