Evidence map›Paper›PMID 34277880›Full record

ReviewOrthopaedic journal of sports medicine2021

Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries.

Jason Corban, Justin-Pierre Lorange, Carl Laverdiere, Jason Khoury, Gil Rachevsky, Mark Burman, Paul Andre Martineau

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Orthopaedic journal of sports medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  8. Machine learning model identifies tibial anatomical variables as potential risk factors for anterior cruciate ligament injury.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jason CorbanDivision of Orthopaedic Surgery, Department of Surgery, McGill University, Montreal, Quebec, Canada.
Justin-Pierre LorangeFaculty of Medicine, McGill University, Montreal, Quebec, Canada.
Carl LaverdiereFaculty of Medicine, McGill University, Montreal, Quebec, Canada.
Jason KhouryDivision of Orthopaedic Surgery, Department of Surgery, McGill University, Montreal, Quebec, Canada.
Gil RachevskyDivision of Orthopaedic Surgery, Department of Surgery, McGill University, Montreal, Quebec, Canada.
Mark BurmanDivision of Orthopaedic Surgery, Department of Surgery, McGill University, Montreal, Quebec, Canada.
Paul Andre MartineauDivision of Orthopaedic Surgery, Department of Surgery, McGill University, Montreal, Quebec, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTechnological 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

designSystematic review; Level of evidence, 3.

methodsA 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.

resultsA 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.

conclusionThere 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.

Indexed as

anterior cruciate ligamentgait analysisgeneralimaging and radiologyinjury preventionphysical therapy/rehabilitation

Identifiers

PMID34277880
PMCPMC8255602

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.