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. 2023 Aug 21;18(8):e0282346.
doi: 10.1371/journal.pone.0282346. eCollection 2023.

Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study

Daniel L Belavy  1 , Scott D Tagliaferri  2 , Martin Tegenthoff  3 , Elena Enax-Krumova  3 , Lara Schlaffke  3 , Björn Bühring  4 , Tobias L Schulte  5 , Sein Schmidt  6 , Hans-Joachim Wilke  7 , Maia Angelova  8 , Guy Trudel  9 , Katja Ehrenbrusthoff  1 , Bernadette Fitzgibbon  10   11   12 , Jessica Van Oosterwijck  13 , Clint T Miller  2 , Patrick J Owen  2 , Steven Bowe  14   15 , Rebekka Döding  1 , Svenja Kaczorowski  1
Affiliations

Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study

Daniel L Belavy et al. PLoS One. .

Abstract

In patients presenting with low back pain (LBP), once specific causes are excluded (fracture, infection, inflammatory arthritis, cancer, cauda equina and radiculopathy) many clinicians pose a diagnosis of non-specific LBP. Accordingly, current management of non-specific LBP is generic. There is a need for a classification of non-specific LBP that is both data- and evidence-based assessing multi-dimensional pain-related factors in a large sample size. The "PRedictive Evidence Driven Intelligent Classification Tool for Low Back Pain" (PREDICT-LBP) project is a prospective cross-sectional study which will compare 300 women and men with non-specific LBP (aged 18-55 years) with 100 matched referents without a history of LBP. Participants will be recruited from the general public and local medical facilities. Data will be collected on spinal tissue (intervertebral disc composition and morphology, vertebral fat fraction and paraspinal muscle size and composition via magnetic resonance imaging [MRI]), central nervous system adaptation (pain thresholds, temporal summation of pain, brain resting state functional connectivity, structural connectivity and regional volumes via MRI), psychosocial factors (e.g. depression, anxiety) and other musculoskeletal pain symptoms. Dimensionality reduction, cluster validation and fuzzy c-means clustering methods, classification models, and relevant sensitivity analyses, will classify non-specific LBP patients into sub-groups. This project represents a first personalised diagnostic approach to non-specific LBP, with potential for widespread uptake in clinical practice. This project will provide evidence to support clinical trials assessing specific treatments approaches for potential subgroups of patients with non-specific LBP. The classification tool may lead to better patient outcomes and reduction in economic costs.

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Conflict of interest statement

DLB, ST, MT, LS, SeS, HJW, MA, GT, KE, BF, JVO, CTM, PJO, SB, RD, SK declare no conflicts of interest. BB: declares no conflicts of interest relevant to the current work, but has previously received research support, consultancy fees and/or honoraria from AbbVie, Amgen, Biogen, GE/Lunar, Janssen, Galapagos, Gilead, Medimaps, MSD, Sanofi Genzyme, Theramex, UCB. TS: declares prior consulting fees from Johnson & Johnson, Amgen, Kaia Health Software, SpineArt, Implantcast and speaking and travel arrangements from Johnson & Johnson, ICOTEC, Nuvasive, Ulrichmedical, Silony, Amgen, Kaia Health Software. EE-K has received fees from painCert GmbH, Casquar GmbH and Omega Pharma GmbH, outside the submitted work. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Approach to artificial intelligence tool implementation.
GOÄ: Gebührenordnung für Ärzte (German medical cost database).
See this image and copyright information in PMC

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