An Unsupervised Learning Approach for Multimodal Low Back Pain Stratification

Clinical Snapshot

Key Findings

• P Value: P < 0.05 for subgroup characterisation

• Effect Size: Significant group differences (P < 0.05) for psychosocial and degenerative characteristics

• Primary Outcome: Three distinct LBP subgroups identified: psychosocial-dominant, physical degenerative-dominant, and mixed characteristics

• Nnt Or Sensitivity: Predictive accuracy: STarT Back-based model 0.89, ÖMPSQ-based model 0.87

• Confidence Interval: Not explicitly reported in abstract

Clinical Application

Requires MRI access and deep learning infrastructure, potentially limiting routine clinical application Relevant to Australian LBP management but would require validation in local population. STarT Back Tool already used in some Australian primary care settings. MRI access through Medicare may support implementation in specialist care. Adults with low back pain in primary care and specialist settings

Abstract

STUDY DESIGN: Cross-sectional study. OBJECTIVE: This study proposes a novel stratification framework for individuals with low back pain (LBP). The method integrates Northern Finland Birth Cohort data comprising imaging biomarkers from deep learning (DL)-based analysis of lumbar spine MRI with the data on smoking status, demographics (sex and BMI), self-reported data from Örebro Musculoskeletal Pain Screening Questionnaire (ÖMPSQ) short and the STarT Back Tool (SBT). Furthermore, the utility of this stratified approach was validated by demonstrating a superior net benefit compared with "treat-all" strategy. BACKGROUND: Current risk stratification for individuals with LBP relies on ÖMPSQ short and SBT among others. While these tools are invaluable for capturing psychosocial characteristics predictive of future disability and functional outcomes, LBP's multifactorial nature necessitates a more comprehensive framework for effective risk stratification. MATERIALS AND METHODS: A method for multimodal unsupervised patient stratification has been developed that allows for the integration of imaging biomarkers of disc degeneration (DD) and facet tropism (FT), extracted using DL models, with nonimaging data. The framework utilized robust K-Means clustering to stratify individuals. Clusters were characterized using LBP frequency and bothersomeness, and their robustness was validated with a multi-class logistic regression model. Net benefit was assessed through decision curve analysis. RESULTS: Three distinct subgroups were characterized by LBP frequency and bothersomeness. One subgroup was dominated by psychosocial characteristics (psychosocial risk P  < 0.05), the second by physical degenerative changes (DD P  < 0.05), and the third by a mix of both. Predictive models for cluster assignment were robust, achieving high mean accuracies (SBT-based: 0.89; ÖMPSQ-short-based: 0.87). The net benefit is superior throughout a range of threshold probabilities compared with a "treat-all" strategy. CONCLUSION: A novel framework was developed that integrates multimodal data to identify distinct subgroups differentiated by their physical and psychosocial characteristics in a population-based cohort, demonstrating potential for advancing personalized care.

References

1. 1.Kowlagi, N., Heikkala, E., Saarakkala, S., Karppinen, J., & Tiulpin, A. (2026). An unsupervised learning approach for multimodal low back pain stratification. Spine. https://doi.org/10.1001/jama.2015.37