Artificial Intelligence in Spine and Paraspinal Muscle Analysis

James M. Elliott*, Evert O. Wesselink, Rebecca J. Crawford, Jon Cornwall, Marnee McKay, Zachary Smith, Kenneth A. Weber

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

Disorders affecting the neurological and musculoskeletal systems represent international health burdens. A significant impediment to progress with interventional trials is the absence of responsive, objective, and valid outcome measures sensitive to early disease or disorder change. A key finding in individuals with spinal disorders is compositional changes to the paraspinal muscle and soft tissue (e.g., intervertebral disc, facet joint capsule, and ligamentous) structure. Quantification of paraspinal muscle composition by MRI has emerged as a sensitive marker for the severity of these conditions; however, little is known about the composition of muscles across the lifespan. Knowledge of what is “typical” age-related muscle composition is essential in order to accurately identify and evaluate “atypical,” with a potential impact being improvements in pre- and postsurgical plan and measurement of surgical implants, exoskeletons, and care on a patient-by-patient basis.

Original languageEnglish
Title of host publicationComputational Neurosurgery
EditorsAntonio Di Ieva, Eric Suero Molina, Sidong Liu, Carlo Russo
PublisherSpringer Nature
Pages465-473
Number of pages9
ISBN (Electronic)9783031648922
ISBN (Print)9783031648915, 9783031648946
DOIs
Publication statusPublished - 2024

Publication series

NameAdvances in Experimental Medicine and Biology
PublisherSpringer
Volume1462
ISSN (Print)0065-2598
ISSN (Electronic)2214-8019

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

  • Biological aging
  • Convolutional neural networks
  • MRI
  • Skeletal muscle
  • Spine

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