Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries.
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 solv...
| Publicado en: | Orthopaedic Journal of Sports Medicine Vol. 9; no. 7; pp. 1 - 13 |
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| Autores principales: | , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Jul2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151723222&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151723222 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23259671 FUGT jtl: Orthopaedic Journal of Sports Medicine issn: 23259671 maglogo: Y pubinfo: dt: Jul2021 vid: 9 iid: 7 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 151723222 151723222 151723222 10.1177/23259671211014206 151723222 ppf: 1 ppct: 12 formats: tig: atl: Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries. aug: au: Corban, Jason Lorange, Justin-Pierre Laverdiere, Carl Khoury, Jason Rachevsky, Gil Burman, Mark Martineau, Paul Andre affil: Division of Orthopaedic Surgery, Department of Surgery, 5620McGill University, Montreal, Quebec, Canada. sug: subj: Artificial Intelligence Anterior Cruciate Ligament Injuries Therapy Machine Learning Anterior Cruciate Ligament Injuries Diagnosis Anterior Cruciate Ligament Injuries Rehabilitation Orthopedic Surgery PubMed Medline Embase SportDiscus Descriptive Statistics Systematic Review Human ab: 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. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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