Study design: Retrospective study.
Objective: Lumbar magnetic resonance imaging (MRI) findings are believed to be associated with low back pain (LBP). This study sought to develop a new predictive classification system for low back pain.
Method: Normal subjects with repeated lumbar MRI scans were retrospectively enrolled. A new classification system, based on the radiological features on MRI, was developed using an unsupervised clustering method.
Results: One hundred and fifty-nine subjects were included. Three distinguishable clusters were identified with unsupervised clustering that were significantly correlated with LBP (P = .017). The incidence of LBP was highest in cluster 3 (57.14%), nearly twice the incidence in cluster 1 (30.11%). There were obvious differences in the sagittal parameters among the 3 clusters. Cluster 3 had the smallest intervertebral height. Based on follow-up findings, 27% of subjects changed clusters. More subjects changed from cluster 1 to clusters 2 or 3 (14.5%) than changed from cluster 2 or cluster 3 to cluster 1 (5%). Participation in sport was more frequent in subjects who changed from cluster 3 to cluster 1.
Conclusion: Using an unsupervised clustering method, we developed a new classification system comprising 3 clusters, which were significantly correlated with LBP. The prediction of LBP is independent of age and better than that based on individual sagittal parameters derived from MRI. A change in cluster during follow-up may partially predict lumbar degeneration. This study provides a new system for the prediction of LBP that should be useful for its diagnosis and treatment.
Keywords: MRI; low back pain; lumbar degeneration; machine learning; unsupervised clustering.