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

ABSTRACT: 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 and supervised learning was used for prediction model Support Vector Machine and Deep Network architectures were compared. A M-fold cross validation procedure was performed to evaluate the results. The accuracy and balanced accuracy of the best supervised model were close to 85%. Classification rates by class were measured using the confusion matrix, giving a low percentage of unclassified patients. Rasterstereography has turned out to be a good tool to distinguish subject with scoliosis from heal

SUBMITTER: Colombo T 

PROVIDER: S-EPMC8699618 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature