Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries

• Published in: Orthopaedic Journal of Sports Medicine Vol. 9; no. 7; p. 23259671211014206
• Main Authors: Corban, Jason, Lorange, Justin-Pierre, Laverdiere, Carl, Khoury, Jason, Rachevsky, Gil, Burman, Mark, Martineau, Paul Andre
• Format: Book Review Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.07.2021; Sage Publications Ltd
• Subjects: Artificial intelligence; Joint and ligament injuries; Knee; Orthopedics; Postoperative period; Sports injuries; Sports medicine; Systematic review; general; imaging and radiology; anterior cruciate ligament; injury prevention; physical therapy/rehabilitation; gait analysis
• ISSN: 2325-9671; 2325-9671
• DOI: 10.1177/23259671211014206

Background: Technological innovation is a key component of orthopaedic surgery. With the integration of powerful technologies in surgery and clinical practice, artificial intelligence (AI) may become an important tool for orthopaedic surgeons in the future. Through adaptive learning and problem solving that serve to constantly increase accuracy, machine learning algorithms show great promise in orthopaedics. Purpose: To investigate the current and potential uses of AI in the management of anterior cruciate ligament (ACL) injury. Study Design: Systematic review; Level of evidence, 3. Methods: A systematic review of the PubMed, MEDLINE, Embase, Web of Science, and SPORTDiscus databases between their start and August 12, 2020, was performed by 2 independent reviewers. Inclusion criteria included application of AI anywhere along the spectrum of predicting, diagnosing, and managing ACL injuries. Exclusion criteria included non-English publications, conference abstracts, review articles, and meta-analyses. Statistical analysis could not be performed because of data heterogeneity; therefore, a descriptive analysis was undertaken. Results: A total of 19 publications were included after screening. Applications were divided based on the different stages of the clinical course in ACL injury: prediction (n = 2), diagnosis (n = 12), intraoperative application (n = 1), and postoperative care and rehabilitation (n = 4). AI-based technologies were used in a wide variety of applications, including image interpretation, automated chart review, assistance in the physical examination via optical tracking using infrared cameras or electromagnetic sensors, generation of predictive models, and optimization of postoperative care and rehabilitation. Conclusion: There is an increasing interest in AI among orthopaedic surgeons, as reflected by the applications for ACL injury presented in this review. Although some studies showed similar or better outcomes using AI compared with traditional techniques, many challenges need to be addressed before this technology is ready for widespread use.