Machine learning identifies clusters of the normal adolescent spine based on sagittal balance-Reference-Cited by-同舟云学术

Machine learning identifies clusters of the normal adolescent spine based on sagittal balance

Published:2024-08-21 Issue: Volume: Page:
ISSN:2212-134X
Container-title:Spine Deformity
language:en
Short-container-title:Spine Deform

Author:

Birhiray Dion G.^ORCID,Chilukuri Srikhar V.,Witsken Caleb C.,Wang Maggie,Scioscia Jacob P.,Gehrchen Martin,Deveza Lorenzo R.,Dahl Benny

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s43390-024-00952-6.pdf

Reference28 articles.

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2. Kim D, Davis DD, Menger RP. (2024) Spine Sagittal Balance. In: StatPearls. StatPearls Publishing;. http://www.ncbi.nlm.nih.gov/books/NBK534858/Accessed 28 Feb 2024

3. Ames CP, Smith JS, Pellisé F et al (2019) Artificial intelligence based hierarchical clustering of patient types and intervention categories in adult spinal deformity surgery: towards a new classification scheme that predicts quality and value. Spine 44(13):915–926. https://doi.org/10.1097/BRS.0000000000002974

4. Ohyama S, Maki S, Kotani T et al (2024) Machine learning algorithms for predicting Cobb angle beyond 25 degrees in female adolescent idiopathic scoliosis patients. Spine. https://doi.org/10.1097/BRS.0000000000004986

5. Laouissat F, Sebaaly A, Gehrchen M, Roussouly P (2018) Classification of normal sagittal spine alignment: refounding the Roussouly classification. Eur Spine J Off Publ Eur Spine Soc Eur Spinal Deform Soc Eur Sect Cerv Spine Res Soc. https://doi.org/10.1007/s00586-017-5111-x