Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review

Abstract

Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Computer Aided Diagnosis”, “Low Back Pain”, “Lumbar”, “Intervertebral Disc Degeneration”, “Spine Surgery”, etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.

Keywords: artificial intelligence; computer aided diagnosis; decision support systems; deep learning; low back pain; orthopaedics.

Figure 1

Partitioning of the studies concerning the application of AI in LBP, presented in [8].

Figure 2

Summary of the methodological quality of included studies regarding the four domains assessing the risk of bias (left) and the three domains assessing applicability concerns (right) of the QUADAS-2 score. The portion of studies with a low risk of bias is highlighted in green, the portion with an unclear risk of bias is depicted in blue, and the portion with a high risk of bias is represented in orange.

Figure 3

Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram.

Figure 4

Accuracy of the LBP diagnosis task of studies using different features, reported on the vertical axis, and both deep learning (red asterisks), machine learning (blue circles) or both (black square) approaches.

Figure 5

Boxplot reporting the accuracy of the disc degeneration classification task of studies that used machine learning (left) or deep learning (right) approaches.