IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i10p5971-d815609.html
   My bibliography  Save this article

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

Author

Listed:
  • Federico D’Antoni

    (Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy)

  • Fabrizio Russo

    (Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy)

  • Luca Ambrosio

    (Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy)

  • Luca Bacco

    (Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy
    ItaliaNLP Lab, Istituto di Linguistica Computazionale “Antonio Zampolli”, National Research Council, Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy
    Webmonks S.r.l., Via del Triopio, 5, 00178 Rome, Italy)

  • Luca Vollero

    (Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy)

  • Gianluca Vadalà

    (Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy)

  • Mario Merone

    (Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy)

  • Rocco Papalia

    (Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy)

  • Vincenzo Denaro

    (Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy)

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.

Suggested Citation

  • Federico D’Antoni & Fabrizio Russo & Luca Ambrosio & Luca Bacco & Luca Vollero & Gianluca Vadalà & Mario Merone & Rocco Papalia & Vincenzo Denaro, 2022. "Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review," IJERPH, MDPI, vol. 19(10), pages 1-20, May.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:5971-:d:815609
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/10/5971/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/10/5971/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fabrizio Russo & Sergio De Salvatore & Luca Ambrosio & Gianluca Vadalà & Luca Fontana & Rocco Papalia & Jorma Rantanen & Sergio Iavicoli & Vincenzo Denaro, 2021. "Does Workers’ Compensation Status Affect Outcomes after Lumbar Spine Surgery? A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 18(11), pages 1-21, June.
    2. Federico D’Antoni & Fabrizio Russo & Luca Ambrosio & Luca Vollero & Gianluca Vadalà & Mario Merone & Rocco Papalia & Vincenzo Denaro, 2021. "Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review," IJERPH, MDPI, vol. 18(20), pages 1-21, October.
    3. Friska Natalia & Hira Meidia & Nunik Afriliana & Julio Christian Young & Reyhan Eddy Yunus & Mohammed Al-Jumaily & Ala Al-Kafri & Sud Sudirman, 2020. "Automated measurement of anteroposterior diameter and foraminal widths in MRI images for lumbar spinal stenosis diagnosis," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-27, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xin Li & Michael Yi-chao Jiang & Morris Siu-yung Jong & Xinping Zhang & Ching-sing Chai, 2022. "Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study," IJERPH, MDPI, vol. 19(14), pages 1-17, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giorgia Petrucci & Giuseppe Francesco Papalia & Fabrizio Russo & Gianluca Vadalà & Michela Piredda & Maria Grazia De Marinis & Rocco Papalia & Vincenzo Denaro, 2021. "Psychological Approaches for the Integrative Care of Chronic Low Back Pain: A Systematic Review and Metanalysis," IJERPH, MDPI, vol. 19(1), pages 1-19, December.
    2. Federico D’Antoni & Fabrizio Russo & Luca Ambrosio & Luca Vollero & Gianluca Vadalà & Mario Merone & Rocco Papalia & Vincenzo Denaro, 2021. "Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review," IJERPH, MDPI, vol. 18(20), pages 1-21, October.
    3. Fabrizio Russo & Giuseppe Francesco Papalia & Gianluca Vadalà & Luca Fontana & Sergio Iavicoli & Rocco Papalia & Vincenzo Denaro, 2021. "The Effects of Workplace Interventions on Low Back Pain in Workers: A Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 18(23), pages 1-17, November.
    4. Friska Natalia & Julio Christian Young & Nunik Afriliana & Hira Meidia & Reyhan Eddy Yunus & Sud Sudirman, 2022. "Automated selection of mid-height intervertebral disc slice in traverse lumbar spine MRI using a combination of deep learning feature and machine learning classifier," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-30, January.
    5. Giuseppe Francesco Papalia & Giorgia Petrucci & Fabrizio Russo & Luca Ambrosio & Gianluca Vadalà & Sergio Iavicoli & Rocco Papalia & Vincenzo Denaro, 2022. "COVID-19 Pandemic Increases the Impact of Low Back Pain: A Systematic Review and Metanalysis," IJERPH, MDPI, vol. 19(8), pages 1-11, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:10:p:5971-:d:815609. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.