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RT Journal Article
SR Electronic
T1 Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 1586
OP 1591
DO 10.3174/ajnr.A6174
VO 40
IS 9
A1 Gaonkar, B.
A1 Villaroman, D.
A1 Beckett, J.
A1 Ahn, C.
A1 Attiah, M.
A1 Babayan, D.
A1 Villablanca, J.P.
A1 Salamon, N.
A1 Bui, A.
A1 Macyszyn, L.
YR 2019
UL http://www.ajnr.org/content/40/9/1586.abstract
AB 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 &lt; .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.CPTCurrent Procedural TerminologyERTensemble of regression treesICD-9International Classification of DiseasesMLmachine learningMRNmedical record numberSVMsupport vector machine