Imaging Biomarker Development for Lower Back Pain Using Machine Learning: How Image Analysis Can Help Back Pain.
Methods Mol Biol
; 2393: 623-640, 2022.
Article
in En
| MEDLINE
| ID: mdl-34837203
ABSTRACT
State-of-the-art diagnosis of radiculopathy relies on "highly subjective" radiologist interpretation of magnetic resonance imaging of the lower back. Currently, the treatment of lumbar radiculopathy and associated lower back pain lacks coherence due to an absence of reliable, objective diagnostic biomarkers. Using emerging machine learning techniques, the subjectivity of interpretation may be replaced by the objectivity of automated analysis. However, training computer vision methods requires a curated database of imaging data containing anatomical delineations vetted by a team of human experts. In this chapter, we outline our efforts to develop such a database of curated imaging data alongside the required delineations. We detail the processes involved in data acquisition and subsequent annotation. Then we explain how the resulting database can be utilized to develop a machine learning-based objective imaging biomarker. Finally, we present an explanation of how we validate our machine learning-based anatomy delineation algorithms. Ultimately, we hope to allow validated machine learning models to be used to generate objective biomarkers from imaging data-for clinical use to diagnose lumbar radiculopathy and guide associated treatment plans.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Low Back Pain
Limits:
Humans
Language:
En
Journal:
Methods Mol Biol
Journal subject:
BIOLOGIA MOLECULAR
Year:
2022
Document type:
Article
Affiliation country:
United States