Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis.
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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 healthy patients limiting the exposure to unnecessary radiations.
SUBMITTER: Colombo T
PROVIDER: S-EPMC8699618 | biostudies-literature |
REPOSITORIES: biostudies-literature
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