Abstract
BACKGROUND AND PURPOSE: Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay the groundwork to establish such biomarkers by leveraging the developments in machine learning and medical imaging informatics.
MATERIALS AND METHODS: Machine learning algorithms were trained to segment lumbar spinal canal areas on axial views and intervertebral discs on sagittal views of lumbar MRIs. These were used to measure spinal canal areas at each lumbar level (L1 through L5). Machine-generated delineations were compared with 2 sets of human-generated delineations to validate the proposed techniques. Then, we use these machine learning methods to delineate and measure lumbar spinal canal areas in a normative cohort and to analyze their variation with respect to age, sex, and height using a variable-intercept mixed model.
RESULTS: We established that machine-generated delineations are comparable with human-generated segmentations. Spinal canal areas as measured by machine are statistically significantly correlated with height (P < .05) but not with age or sex.
CONCLUSIONS: Our machine learning methodology demonstrates that this important anatomic structure can be accurately detected and quantitatively measured without human input in a manner comparable with that of human raters. Anatomic deviations measured against the normative model established here could be used to flag spinal stenosis in the future.
ABBREVIATIONS:
- CPT
- Current Procedural Terminology
- ERT
- ensemble of regression trees
- ICD-9
- International Classification of Diseases
- ML
- machine learning
- MRN
- medical record number
- SVM
- support vector machine
Footnotes
Disclosures: Bilwaj Gaonkar—UNRELATED: Employment: University of California Los Angeles, RELATED: Passive stock ownership in Theseus AI, a start up being spun out of University of California Los Angeles, Luke Macyszyn - UNRELATED: Employment: University of California Los Angeles, RELATED: Stock ownership and Board membership in Theseus AI, a start up being spun out of University of California Los Angeles, NIH grant R21EB026665 awardeed as Principal Investigator; We have plans to apply for patents (Planned, Pending or Issued) and wish to have the right to submit patents based on this work in the future.
- © 2019 by American Journal of Neuroradiology