The role of Artificial intelligence in the assessment of the spine and spinal cord.

Journal: European journal of radiology
Published Date:

Abstract

Artificial intelligence (AI) application development is underway in all areas of radiology where many promising tools are focused on the spine and spinal cord. In the past decade, multiple spine AI algorithms have been created based on radiographs, computed tomography, and magnetic resonance imaging. These algorithms have wide-ranging purposes including automatic labeling of vertebral levels, automated description of disc degenerative changes, detection and classification of spine trauma, identification of osseous lesions, and the assessment of cord pathology. The overarching goals for these algorithms include improved patient throughput, reducing radiologist workload burden, and improving diagnostic accuracy. There are several pre-requisite tasks required in order to achieve these goals, such as automatic image segmentation, facilitating image acquisition and postprocessing. In this narrative review, we discuss some of the important imaging AI solutions that have been developed for the assessment of the spine and spinal cord. We focus on their practical applications and briefly discuss some key requirements for the successful integration of these tools into practice. The potential impact of AI in the imaging assessment of the spine and cord is vast and promises to provide broad reaching improvements for clinicians, radiologists, and patients alike.

Authors

  • Teodoro Martín-Noguerol
    MRI Unit, Radiology Department, HT médica Carmelo Torres 2, Jaén 23007, Spain. Electronic address: t.martin.f@htime.org.
  • Marta Oñate Miranda
    Department of Radiology, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, Quebec, Canada. Electronic address: marta.onate.miranda@usherbrooke.ca.
  • Timothy J Amrhein
    3 Department of Radiology, Duke University Medical Center, Durham, NC.
  • Félix Paulano-Godino
    3D Printing Unit, Engineering Department, Health Time, Jaén, Spain.
  • Pau Xiberta
    Graphics and Imaging Laboratory (GILAB), University of Girona, 17003 Girona, Spain. Electronic address: pau.xiberta@udg.edu.
  • Joan C Vilanova
    Girona Magnetic Resonance Center, Spain.
  • Antonio Luna
    MRI Unit, Radiology Department, Health Time, Jaén, Spain. Electronic address: aluna70@htime.org.