Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review
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...
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BMC
2025-02-01
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| Series: | BMC Musculoskeletal Disorders |
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| Online Access: | https://doi.org/10.1186/s12891-025-08356-x |
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| author | 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 |
| author_facet | 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 |
| author_sort | Paolo Giaccone |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1a60f8449b5246e4a0c1742d0ad17bea |
| institution | Kabale University |
| issn | 1471-2474 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Musculoskeletal Disorders |
| spelling | doaj-art-1a60f8449b5246e4a0c1742d0ad17bea2025-02-09T12:04:22ZengBMCBMC Musculoskeletal Disorders1471-24742025-02-0126111410.1186/s12891-025-08356-xPrevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic reviewPaolo Giaccone0Federico D’Antoni1Fabrizio Russo2Luca Ambrosio3Giuseppe Francesco Papalia4Onorato d’Angelis5Gianluca Vadalà6Albert Comelli7Luca Vollero8Mario Merone9Rocco Papalia10Vincenzo Denaro11Fondazione Policlinico Universitario Campus Bio-MedicoFondazione Policlinico Universitario Campus Bio-MedicoOperative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-MedicoOperative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-MedicoOperative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-MedicoResearch Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di RomaOperative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-MedicoRi.MED FoundationResearch Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di RomaResearch Unit of Intelligent Technology for Health and Wellbeing, Department of Engineering, Università Campus Bio-Medico di RomaOperative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-MedicoOperative Research Unit of Orthopaedic and Trauma Surgery, Fondazione Policlinico Universitario Campus Bio-MedicoAbstract 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.https://doi.org/10.1186/s12891-025-08356-xArtificial intelligenceMachine learningDeep learningLow back painSpinePrevention |
| spellingShingle | 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 Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review BMC Musculoskeletal Disorders Artificial intelligence Machine learning Deep learning Low back pain Spine Prevention |
| title | Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review |
| title_full | Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review |
| title_fullStr | Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review |
| title_full_unstemmed | Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review |
| title_short | Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review |
| title_sort | prevention and management of degenerative lumbar spine disorders through artificial intelligence based decision support systems a systematic review |
| topic | Artificial intelligence Machine learning Deep learning Low back pain Spine Prevention |
| url | https://doi.org/10.1186/s12891-025-08356-x |
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