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

by Daniel L. Belavy, Scott D. Tagliaferri, Martin Tegenthoff, Elena Enax-Krumova, Lara Schlaffke, Bj örn Bühring, Tobias L. Schulte, Sein Schmidt, Hans-Joachim Wilke, Maia Angelova, Guy Trudel, Katja Ehrenbrusthoff, Bernadette Fitzgibbon, Jessica Van Oosterwijck, Clint T. Miller, Patrick J. Owen, Steven Bowe, Rebekka Döding, Svenja Kaczorowski 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 s tudy 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, structura...
Source: PLoS One - Category: Biomedical Science Authors: Source Type: research