Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis

The aim of our study was to classify scoliosis compared to to healthy patients using non-invasive surface acquisition via Video-raster-stereography, without prior knowledge of radiographic data. Data acquisitions were made using Rasterstereography; unsupervised learning was adopted for clustering an...

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Detalles Bibliográficos
Autores principales: Colombo, Tommaso, Mangone, Massimiliano, Agostini, Francesco, Bernetti, Andrea, Paoloni, Marco, Santilli, Valter, Palagi, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699618/
https://www.ncbi.nlm.nih.gov/pubmed/34941924
http://dx.doi.org/10.1371/journal.pone.0261511

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699618/
https://www.ncbi.nlm.nih.gov/pubmed/34941924
http://dx.doi.org/10.1371/journal.pone.0261511

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