Abstract
BackgroundMRI scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS). However, there is currently no consensus as to how best to classify MRI findings which has hampered the development of robust longitudinal epidemiological studies of the condition. We developed and tested an automated system for grading lumbar spine MRI scans for central LSS for use in epidemiological research.MethodsUsing MRI scans from the large population-based cohort study (the Wakayama Spine Study), all graded by a spinal surgeon, we trained an automated system to grade central LSS in four gradings of the bone and soft tissue margins: none, mild, moderate, severe. Subsequently, we tested the automated grading against the independent readings of our observer in a test set to investigate reliability and agreement.ResultsComplete axial views were available for 4855 lumbar intervertebral levels from 971 participants. The machine used 4365 axial views to learn (training set) and graded the remaining 490 axial views (testing set). The agreement rate for gradings was 65.7% (322/490) and the reliability (Lin’s correlation coefficient) was 0.73. In 2.2% of scans (11/490) there was a difference in classification of 2 and in only 0.2% (1/490) was there a difference of 3. When classified into 2 groups as ‘severe’ vs ‘no/mild/moderate’. The agreement rate was 94.1% (461/490) with a kappa of 0.75.ConclusionsThis study showed that an automated system can “learn” to grade central LSS with excellent performance against the reference standard. Thus SpineNet offers potential to grade LSS in large-scale epidemiological studies involving a high volume of MRI spine data with a high level of consistency and objectivity.
Highlights
Magnetic resonance Imaging (MRI) scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS)
Full list of author information is available at the end of the article
Using two sets of axial scans taken as part of the baseline of the Wakayama Spine Study (WSS), we investigated the ability of the SpineNet system to “learn” to grade central LSS in comparison with the qualitative assessment of the trained surgeon
Summary
MRI scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS). There is currently no consensus as to how best to classify MRI findings which has hampered the development of robust longitudinal epidemiological studies of the condition. We developed and tested an automated system for grading lumbar spine MRI scans for central LSS for use in epidemiological research. There is to date no consensus as to how to define LSS severity on MRI scans [7] and a number of qualitative approaches have been suggested [8, 9]. The relationship between findings on MRI and clinical course is the source of some controversy with several studies suggesting a high prevalence of MRI LSS in asymptomatic subjects [10, 11]
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