Clinical classification of scoliosis patients using machine learning and markerless 3D surface trunk data.
- DOI
- 10.1007/s11517-020-02258-x
- Published
- 2020 Dec
- Container
- Medical & biological engineering & computing
- Publisher
- Not recorded
- Open access
- unknown
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limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1007/s11517-020-02258-x,
title = {Clinical classification of scoliosis patients using machine learning and markerless 3D surface trunk data.},
author = {Rothstock S and Weiss HR and Krueger D and Paul L},
year = {2020},
journal = {Medical \& biological engineering \& computing},
doi = {10.1007/s11517-020-02258-x},
url = {https://doi.org/10.1007/s11517-020-02258-x}
}RIS
TY - JOUR TI - Clinical classification of scoliosis patients using machine learning and markerless 3D surface trunk data. AU - Rothstock S AU - Weiss HR AU - Krueger D AU - Paul L PY - 2020 JO - Medical & biological engineering & computing DO - 10.1007/s11517-020-02258-x UR - https://doi.org/10.1007/s11517-020-02258-x ER -
APA
S, R., HR, W., D, K., & L, P. (2020). Clinical classification of scoliosis patients using machine learning and markerless 3D surface trunk data.. Medical & biological engineering & computing. https://doi.org/10.1007/s11517-020-02258-x
Source records
- pubmed · retrieved 2026-09-27T15:08:58.080Z