Low back pain (LBP) is a leading cause of disability worldwide, with up to 25% of cases become chronic (cLBP). Optimal diagnostic tools for cLBP remains unclear. Here we leveraged a comprehensive multidimensional dataset and machine learning based feature importance selection to identify the most effective diagnostic tools for cLBP patient stratification. The dataset included questionnaire data, clinical and functional assessments, and spinopelvic magnetic resonance imaging (MRI), encompassing a total of 144 parameters from 1,161 adults with (n=512) and without cLBP (n=649). Boruta and random forest were utilised for variable importance selection and cLBP classification respectively. Boruta feature selection led to pronounced variable reduction (median of all 15 datasets: 63.3%), while performing comparable to using all variables across all modality datasets. Multimodality models performed better than single modality models. Boruta selected key variables from questionnaire, clinical, and MRI data were the most effective in distinguishing cLBP patients from controls with an AUC (area under the receiver operating characteristic curve) of 0.699 (95% confidence interval [CI], 0.669, 0.729). The most robust features (n=9) across the whole dataset identified were psychosocial factors, neck and hip mobility, as well as lower lumbar disc herniation and degeneration. These critical variables (AUC = 0.664, 95% CI = 0.514, 0.814) outperformed all parameters (AUC = 0.602, 95% CI = 0.538, 0.666) in an unseen holdout dataset, demonstrating superior patient delineation. Paving the way for targeted diagnosis and personalized treatment strategies, ultimately enhancing clinical outcomes for cLBP patients.

The authors have declared no competing interest.

DRKS00027907

This study is part of the Research Unit FOR 5177 funded by the German Research Foundation (DFG), Hendrik Schmidt: SCHM 2572/11-1, SCHM 2572/12-1, SCHM 2572/13-1; Sandra Reitmaeier: RE 4292/3-1, Matthias Pumberger: PU762/1-1. The analyses and contribution from the Hochschule fuer Gesundheit were funded, in part, by grant number 50WK2273A (to DLB) from the German AeroSpace Center (DLR).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The ethics committee of the Charite Universitaetsmedizin Berlin (registry numbers: EA4/011/10, EA1/162/13) gave ethical approval for this work.

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The raw data of this study will be openly released from the Berlin Back Study as per agreement with the funding agency following the completion of the data acquisition (30.12.2025).