Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review - Paolo Giaccone; Federico D’Antoni; Fabrizio Russo; Luca Ambrosio; Giuseppe Francesco Papalia; Onorato d’Angelis; Gianluca Vadalà; Albert Comelli; Luca Vollero; Mario Merone; Rocco Papalia; Vincenzo Denaro (BMC Musculoskeletal Disorders)
Abstract Background Low back pain is the leading cause of disability worldwide with a significant socioeconomic burden; artificial intelligence (AI) has proved to have a great potential in supporting clinical decisions at each stage of the healthcare process. In this article, we have systematically reviewed the available literature on the applications of AI-based Decision Support Systems (DSS) in the clinical prevention and management of Low Back Pain (LBP) due to lumbar degenerative spine disorders. Methods A systematic review of Pubmed and Scopus databases was performed according to the PRISMA statement. Studies reporting the application of DSS to support the prevention and/or management of LBP due to lumbar degenerative diseases were included. The QUADAS-2 tool was utilized to assess the risk of bias in the included studies. The area under the curve (AUC) and accuracy were assessed for each study. Results Twenty five articles met the inclusion criteria. Several different machine learning and deep learning algorithms were employed, and their predictive ability on clinical, demographic, psychosocial, and imaging data was assessed. The included studies mainly encompassed three tasks: clinical score definition, clinical assessment, and eligibility prediction and reached AUC scores of 0.93, 0.99 and 0.95, respectively. Conclusions AI-based DSS applications showed a high degree of accuracy in performing a wide set of different tasks. These findings lay the foundation for further research to improve the current understanding and encourage wider adoption of AI in clinical decision-making.