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Imaging Biomarker Development for Lower Back Pain Using Machine Learning: How Image Analysis Can Help Back Pain.

Bilwaj Gaonkar1, Kirstin Cook2, Bryan Yoo3

  • 1Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, USA. BGaonkar@mednet.ucla.edu.

Methods in Molecular Biology (Clifton, N.J.)
|November 27, 2021
PubMed
Summary

Developing an objective imaging biomarker for lumbar radiculopathy diagnosis using machine learning requires a curated database of annotated magnetic resonance imaging (MRI) data. This approach aims to overcome the subjectivity of current diagnostic methods for lower back pain.

Keywords:
Deep learningDegenerative diseaseImage segmentationMachine learningSpine MRI

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Biomarker Development

Background:

  • Current diagnosis of radiculopathy relies on subjective interpretation of lower back MRI scans.
  • Lack of objective biomarkers hinders coherent treatment for lumbar radiculopathy and associated lower back pain.

Purpose of the Study:

  • To outline the development of a curated imaging database for machine learning applications.
  • To enable the creation of objective imaging biomarkers for diagnosing lumbar radiculopathy.

Main Methods:

  • Data acquisition and expert annotation of lower back MRI scans.
  • Development and validation of machine learning algorithms for anatomical delineation.
  • Utilizing curated data to train computer vision models for automated analysis.

Main Results:

  • A process for creating a curated database of annotated imaging data has been established.
  • Methodology for validating machine learning-based anatomy delineation algorithms is presented.

Conclusions:

  • Validated machine learning models can generate objective biomarkers from imaging data.
  • This approach promises to improve the diagnosis and treatment guidance for lumbar radiculopathy.