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Dettaglio pubblicazione

2021, PLOS ONE, Pages 1-24 (volume: 16)

Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis (01a Articolo in rivista)

Colombo T., Mangone M., Agostini F., Bernetti A., Paoloni M., Santilli V., Palagi L.

The aim of our study was to classify scoliosis compared to to healthy patients using noninvasive surface acquisition via Video-raster-stereography, without prior knowledge of radio-graphic 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.
Gruppo di ricerca: Continuous Optimization
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