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

STUDY DESIGN: Consecutively collected cases. OBJECTIVE: To determine if a machine-learning (ML) program can accurately predict the postoperative thoracic kyphosis through the uninstrumented thoracic spine and pelvic compensation in patients who undergo fusion from the lower thoracic spine (T10 or T1...

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Detalles Bibliográficos
Autores principales: Lee, Nathan J., Sardar, Zeeshan M., Boddapati, Venkat, Mathew, Justin, Cerpa, Meghan, Leung, Eric, Lombardi, Joseph, Lenke, Lawrence G., Lehman, Ronald A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109562/
https://www.ncbi.nlm.nih.gov/pubmed/33030054
http://dx.doi.org/10.1177/2192568220956978

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109562/
https://www.ncbi.nlm.nih.gov/pubmed/33030054
http://dx.doi.org/10.1177/2192568220956978

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