Assessment and prediction of spine surgery invasiveness with machine learning techniques.

Journal: Computers in biology and medicine
Published Date: Jun 1, 2020

Abstract

BACKGROUND: The interest in Minimally Invasive Surgery (MIS) techniques has greatly increased in the recent years due to their significant advantages, both in terms of outcome improvement and cost reduction. Also in spine surgery, MIS is now applicable to several conditions and, above all, in low back pain (LBP) treatment. However, reliable and objective measures of invasiveness, necessary to compare different procedures, are still lacking.

Authors

  • Andrea Campagner
    IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milano, Italy. Electronic address: a.campagner@campus.unimib.it.
  • Pedro Berjano
    IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
  • Claudio Lamartina
    IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milano, Italy.
  • Francesco Langella
    IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milano, Italy.
  • Giovanni Lombardi
    Laboratory of Experimental Biochemistry & Molecular Biology, IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi, 4, 20161, Milano, Italy; Department of Athletics, Strength and Conditioning, Poznań University of Physical Education, Królowej Jadwigi 27/39, 61-871, Poznań, Poland.
  • Federico Cabitza
    Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano, Italy.

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