Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol
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(2020) 15:478
STUDY PROTOCOL
Open Access
Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol Nicola Maffulli1,2,3, Hugo C. Rodriguez4,5,6, Ian W. Stone4, Andrew Nam4, Albert Song4, Manu Gupta5, Rebecca Alvarado7, David Ramon7 and Ashim Gupta5,6,8,9*
Abstract Background: Artificial intelligence (AI) and machine learning (ML) are interwoven into our everyday lives and have grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this systematic review will be to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives will be to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery. Methods: A systematic search will be conducted in PubMed, ScienceDirect, and Google Scholar databases for articles written in English, Italian, French, Spanish, and Portuguese language articles published up to September 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions and acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies and do not relate to orthopedic interventions or if no clinical data were produced. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and the results of meta-analyses will not be included in the analysis. Discussion: Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies and a smaller subset of articles describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed. Trial registration: The protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement. Keywords: Machine learning, Artificial intelligence, Orthopedic surgery, PRISMA
* Correspondence: [email protected] 5 Future Biologics LLC, 1110 Ballpark Ln Apt 5109, Lawrenceville, GA 30043, USA 6 South Texas Orthopaedic Research Institute, Laredo, TX, USA Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons At
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