Prediction of spinal curve progression in Adolescent Idiopathic Scoliosis using Random Forest regression

Comput Biol Med. 2018 Dec 1:103:34-43. doi: 10.1016/j.compbiomed.2018.09.029. Epub 2018 Oct 4.


Background: The progression of the spinal curve represents one of the major concerns in the assessment of Adolescent Idiopathic Scoliosis (AIS). The prediction of the shape of the spine from the first visit could guide the management of AIS and provide the right treatment to prevent curve progression.

Method: In this work, we propose a novel approach based on a statistical generative model to predict the shape variation of the spinal curve from the first visit. A spinal curve progression approach is learned using 3D spine models generated from retrospective biplanar X-rays. The prediction is performed every three months from the first visit, for a time lapse of one year and a half. An Independent Component Analysis (ICA) was computed to obtain Independent Components (ICs), which are used to describe the main directions of shape variations. A dataset of 3D shapes of 150 patients with AIS was employed to extract the ICs, which were used to train our approach.

Results: The approach generated an estimation of the shape of the spine through time. The estimated shape differs from the real curvature by 1.83, 5.18, and 4.79° of Cobb angles in the proximal thoracic, main thoracic, and thoraco-lumbar lumbar sections, respectively.

Conclusions: The results obtained from our approach indicate that predictions based on ICs are very promising. ICA offers the means to identify the variation in the 3D space of the evolution of the shape of the spine. Another advantage of using ICs is that they can be visualized for interpretation.

Keywords: Adolescent idiopathic scoliosis; Independent component analysis; Machine learning; Prediction of spinal curve progression; Random forest.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Databases, Factual
  • Decision Trees
  • Disease Progression
  • Humans
  • Imaging, Three-Dimensional / methods*
  • Machine Learning*
  • Radiography / methods*
  • Regression Analysis
  • Scoliosis* / diagnostic imaging
  • Scoliosis* / pathology
  • Thoracic Vertebrae* / diagnostic imaging
  • Thoracic Vertebrae* / pathology