Deep learning prediction of curve severity from rasterstereographic back images in adolescent idiopathic scoliosis.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PMID:

Abstract

PURPOSE: Radiation-free systems based on dorsal surface topography can potentially represent an alternative to radiographic examination for early screening of scoliosis, based on the ability of recognizing the presence of deformity or classifying its severity. This study aims to assess the effectiveness of a deep learning model based on convolutional neural networks in directly predicting the Cobb angle from rasterstereographic images of the back surface in subjects with adolescent idiopathic scoliosis.

Authors

  • Martina Minotti
    IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
  • Stefano Negrini
  • Andrea Cina
    IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161, Milan, Italy.
  • Fabio Galbusera
    Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, Via Galeazzi 4, 20161, Milan, Italy. fabio.galbusera@grupposandonato.it.
  • Fabio Zaina
    ISICO (Italian Scientific Spine Institute), Milan, Italy.
  • Tito Bassani
    Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, Via Galeazzi 4, 20161, Milan, Italy.