Machine Learning Applied to Low Back Pain Rehabilitation – A Systematic Review

Abstract

<strong>Background:</strong> Low back pain (LBP) is the leading cause of disability worldwide and an important cause of work absenteeism in the active population. As a recurrent condition, prevention is crucial. Home exercises are effective, but adherence and accurate performance of the exercises are difficult to monitor by doctors and therapists. Machine learning (ML) applied to rehabilitation systems could be a solution to address telerehabilitation for people with chronic LBP if it holds sufficient accuracy in monitoring adherence performance while providing patient guidance. The aim was to search and review studies that have used ML techniques for rehabilitation of people with LBP. To develop an understanding on the outcomes measured, the clinical setting (face-to-face rehabilitation or remote rehabilitation) where interventions took place, and the clinical research methodology that has been used. <strong>Materials and Methods:</strong> a systematic review was performed based on research material obtained from literature indexed on MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, Web of Science and IEEE Xplore databases to locate papers focused on the use of ML applied to rehabilitation of LBP. <strong>Results:</strong> after revision of the inclusion and exclusion criteria using the PRISMA methodology, only 14 studies remained for the analysis that is presented as a qualitative synthesis. <strong>Conclusions:</strong> ML approaches applied to rehabilitation could help health professionals and LBP patients to manage this condition that affects a significant amount of the active population. ML could be applied to support clinical decisions and to guide patients self-manage their LBP remotely, which makes it a potential telerehabilitation solution. More and better studies, with more participants and following guidelines for best research practice are needed to strengthen the clinical evidence. <strong>HIGHLIGHTS</strong> <ul><li>LBP is the leading cause of disability worldwide</li><li>LBP is a recurrent condition in active population and one of the most common causes of workforce absenteeism, being a significant socio-economic problem</li><li>Home exercises are effective for prevention of LBP</li><li>It is difficult to measure the adherence and the performance accuracy of home exercises</li><li>ML techniques can detect trunk range of motion, muscle recruitment and monitor posture and pain and other LBP-related symptoms that support monitoring and guidance of patient performance in a personalized way</li><li>ML applied to reliable non-invasive monitoring procedures is a promising area to promote self-management of LBP in telerehabilitation scenarios</li></ul><p align="left">