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

Author

Listed:
  • Tommaso Colombo

    (Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy)

  • Massimiliano Mangone

    (Department of Physical Medicine and Rehabilitation, University of Rome La Sapienza, Rome, Italy)

  • Andrea Bernetti

    (Department of Physical Medicine and Rehabilitation, University of Rome La Sapienza, Rome, Italy)

  • Marco Paoloni

    (Department of Physical Medicine and Rehabilitation, University of Rome La Sapienza, Rome, Italy)

  • Valter Santilli

    (Department of Physical Medicine and Rehabilitation, University of Rome La Sapienza, Rome, Italy)

  • Laura Palagi

    (Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy)

Abstract

Objective. Classify scoliosis versus healthy patients using rasterstereography non invasive surface acquisition, without prior knowledge from X-ray data.Methods. Data acquisition via rasterstereography; unsupervised learning for clustering and supervised learning for predicting models. Comparison among Support Vector Machine and Deep Network architectures. K-fold cross validation procedure for assessing the results.Results. The accuracy and the balanced accuracy of the best supervised model was close to 85%. Classification rates per class were measured using confusion matrix giving low percentage of misclassified patients.Conclusion. Rasterstereography turns out to be a good tool to identify scoliosis vs healthy patients with the advantage of not exposing patient to unhealthy X-Ray. Furthermore, thanks to the portability and the low cost of the rasterstereography, it is possible to use it to promote screening campaign.

Suggested Citation

  • Tommaso Colombo & Massimiliano Mangone & Andrea Bernetti & Marco Paoloni & Valter Santilli & Laura Palagi, 2019. "Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis," DIAG Technical Reports 2019-08, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    • Handle: RePEc:aeg:report:2019-08
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    References listed on IDEAS

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    3. Veronica Piccialli & Marco Sciandrone, 2018. "Nonlinear optimization and support vector machines," 4OR, Springer, vol. 16(2), pages 111-149, June.
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