Machine learning in the diagnosis, management, and care of patients with low back pain: a scoping review of the literature and future directions.

Abstract

Background context: Low back pain (LBP) remains the leading cause of disability globally. In recent years, machine learning (ML) has emerged as a potentially useful tool to aid the diagnosis, management, and prognostication of LBP.

Purpose: In this review, we assess the scope of ML applications in the LBP literature and outline gaps and opportunities.

Study design/setting: A scoping review was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines.

Methods: Articles were extracted from the Web of Science, Scopus, PubMed, and IEEE Xplore databases. Title/abstract and full-text screening was performed by two reviewers. Data on model type, model inputs, predicted outcomes, and ML methods were collected.

Results: In total, 223 unique studies published between 1988 and 2023 were identified, with just over 50% focused on low-back-pain detection. Neural networks were used in 106 of these articles. Common inputs included patient history, demographics, and lab values (67% total). Articles published after 2010 were also likely to incorporate imaging data into their models (41.7% of articles). Of the 212 supervised learning articles identified, 168 (79.4%) mentioned use of a training or testing dataset, 116 (54.7%) utilized cross-validation, and 46 (21.7%) implemented hyperparameter optimization. Of all articles, only 8 included external validation and 9 had publicly available code.

Conclusions: Despite the rapid application of ML in LBP research, a majority of articles do not follow standard ML best practices. Furthermore, over 95% of articles cannot be reproduced or authenticated due to lack of code availability. Increased collaboration and code sharing are needed to support future growth and implementation of ML in the care of patients with LBP.