Machine learning accuracy for assessment of functional movement in Low back pain based on clinically applicable
Tamer Burjawi1, Doa El-Ansary2, Joshua Farragher3
1Royal Melbourne Institute of Technology, University, Melbourne, VIC, Australia.
Purpose:
To assess whether machine learning (ML) can accurately evaluate functional kinematics in people with low back pain (LBP) when judged by psychometric properties, including validity, reliability, and measurement error.
Methods:
A systematic search of PubMed, Scopus, Web of Science, and IEEE Xplore identified studies applying ML with kinematic inputs for LBP assessment. Risk of bias was assessed using the Newcastle-Ottawa Scale and selected COSMIN domains.
Results:
Twenty studies met inclusion. Most reported criterion validity via accuracy, while few examined reliability or measurement error. Inertial sensors and support vector machines were the most common methods.
Conclusions:
ML shows strong validity for LBP movement assessment, but limited psychometric reporting constrains clinical use.
