ArticleGlobal spine journal2023
Predictive Classification System for Low Back Pain Based on Unsupervised Clustering.
Article in Global spine journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- Radiographic phenotype-driven clustering in lumbar decompression: comparative study of outcome and reoperation risk.The spine journal : official journal of the North American Spine Society · 2026Article
- Leveraging data to transform surgical outcomes and precision in orthopaedics.Journal of clinical orthopaedics and trauma · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
designRetrospective study.
objectiveLumbar 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.
methodNormal 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.
resultsOne hundred and fifty-nine subjects were included. Three distinguishable clusters were identified with unsupervised clustering that were significantly correlated with LBP (
conclusionUsing 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.
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Registered trials
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