Can Machine Learning Accurately Predict Postoperative Compensation for the Uninstrumented Thoracic Spine and Pelvis After Fusion From the Lower Thoracic Spine to the Sacrum?

Lee, N. J., Sardar, Z. M., Boddapati, V., Mathew, J., Cerpa, M., Leung, E., Lombardi, J., Lenke, L. G., & Lehman, R. A. (2020). Can Machine Learning Accurately Predict Postoperative Compensation for the Uninstrumented Thoracic Spine and Pelvis After Fusion From the Lower Thoracic Spine to the Sacrum? Global Spine Journal, 12(4), 559–566. https://doi.org/10.1177/2192568220956978
Authors:
Nathan J. Lee
Zeeshan M. Sardar
Venkat Boddapati
Justin Mathew
Meghan Cerpa
Eric Leung
Joseph Lombardi
Lawrence G. Lenke
Ronald A. Lehman
Affiliated Authors:
Nathan J. Lee
Zeeshan M. Sardar
Venkat Boddapati
Justin Mathew
Meghan Cerpa
Eric Leung
Joseph Lombardi
Lawrence G. Lenke
Ronald A. Lehman
Columbia Affiliation:
Author Keywords:
adult spinal deformity
compensation
deformity
machine learning
pelvic tilt
proximal junctional kyphosis
spinopelvic
thoracic kyphosis
uninstrumented spine
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Publication Type:
Article
Unique ID:
10.1177/2192568220956978
DOI:
10.1177/2192568220956978
Journal:
Global Spine Journal
Publication Date:
Data Source:
Scopus
Source Link:
View Scopus Record

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