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Review

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

1
Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy
2
Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy
3
ItaliaNLP Lab, Istituto di Linguistica Computazionale “Antonio Zampolli”, National Research Council, Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy
4
Webmonks S.r.l., Via del Triopio, 5, 00178 Rome, Italy
*
Authors to whom correspondence should be addressed.
Academic Editor: Paul B. Tchounwou
Int. J. Environ. Res. Public Health 2022, 19(10), 5971; https://doi.org/10.3390/ijerph19105971
Received: 15 March 2022 / Revised: 9 May 2022 / Accepted: 12 May 2022 / Published: 14 May 2022
(This article belongs to the Special Issue 2nd Edition: Low Back Pain (LBP))
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. View Full-Text
Keywords: low back pain; orthopaedics; artificial intelligence; deep learning; decision support systems; computer aided diagnosis low back pain; orthopaedics; artificial intelligence; deep learning; decision support systems; computer aided diagnosis
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MDPI and ACS Style

D’Antoni, F.; Russo, F.; Ambrosio, L.; Bacco, L.; Vollero, L.; Vadalà, G.; Merone, M.; Papalia, R.; Denaro, V. Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review. Int. J. Environ. Res. Public Health 2022, 19, 5971. https://doi.org/10.3390/ijerph19105971

AMA Style

D’Antoni F, Russo F, Ambrosio L, Bacco L, Vollero L, Vadalà G, Merone M, Papalia R, Denaro V. Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review. International Journal of Environmental Research and Public Health. 2022; 19(10):5971. https://doi.org/10.3390/ijerph19105971

Chicago/Turabian Style

D’Antoni, Federico, Fabrizio Russo, Luca Ambrosio, Luca Bacco, Luca Vollero, Gianluca Vadalà, Mario Merone, Rocco Papalia, and Vincenzo Denaro. 2022. "Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review" International Journal of Environmental Research and Public Health 19, no. 10: 5971. https://doi.org/10.3390/ijerph19105971

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