Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis
Tommaso Colombo, Massimiliano Mangone, Francesco Agostini, Andrea Bernetti, Marco Paoloni, Valter Santilli, Laura Palagi
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.