A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence.

dc.contributor.authorTamimi, Iskandar
dc.contributor.authorBallesteros, Joaquin
dc.contributor.authorPerez-Lara, Almudena
dc.contributor.authorTat, Jimmy
dc.contributor.authorAlaqueel, Motaz
dc.contributor.authorSchupbach, Justin
dc.contributor.authorMarwan, Yousef
dc.contributor.authorUrdiales, Cristina
dc.contributor.authorGomez-de-Gabriel, Jesus Manuel
dc.contributor.authorBurman, Mark
dc.contributor.authorMartineau, Paul Andre
dc.date.accessioned2025-01-07T12:24:00Z
dc.date.available2025-01-07T12:24:00Z
dc.date.issued2021-09-21
dc.description.abstractSupervised machine learning models in artificial intelligence (AI) have been increasingly used to predict different types of events. However, their use in orthopaedic surgery has been limited. It was hypothesized that supervised learning techniques could be used to build a mathematical model to predict primary anterior cruciate ligament (ACL) injuries using a set of morphological features of the knee. Cross-sectional study; Level of evidence, 3. Included were 50 adults who had undergone primary ACL reconstruction between 2008 and 2015. All patients were between 18 and 40 years of age at the time of surgery. Patients with a previous ACL injury, multiligament knee injury, previous ACL reconstruction, history of ACL revision surgery, complete meniscectomy, infection, missing data, and associated fracture were excluded. We also identified 50 sex-matched controls who had not sustained an ACL injury. For all participants, we used the preoperative magnetic resonance images to measure the anteroposterior lengths of the medial and lateral tibial plateaus as well as the lateral and medial bone slope (LBS and MBS), lateral and medial meniscal height (LMH and MMH), and lateral and medial meniscal slope (LMS and MMS). The AI predictor was created using Matlab R2019b. A Gaussian naïve Bayes model was selected to create the predictor. Patients in the ACL injury group had a significantly increased posterior LBS (7.0° ± 4.7° vs 3.9° ± 5.4°; P = .008) and LMS (-1.7° ± 4.8° vs -4.0° ± 4.2°; P = .002) and a lower MMH (5.5 ± 0.1 vs 6.1 ± 0.1 mm; P = .006) and LMH (6.9 ± 0.1 vs 7.6 ± 0.1 mm; P = .001). The AI model selected LBS and MBS as the best possible predictive combination, achieving 70% validation accuracy and 92% testing accuracy. A prediction model for primary ACL injury, created using machine learning techniques, achieved a >90% testing accuracy. Compared with patients who did not sustain an ACL injury, patients with torn ACLs had an increased posterior LBS and LMS and a lower MMH and LMH.
dc.description.versionSi
dc.identifier.citationTamimi I, Ballesteros J, Lara AP, Tat J, Alaqueel M, Schupbach J, et al. A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence. Orthop J Sports Med. 2021 Sep 21;9(9):23259671211027543
dc.identifier.doi10.1177/23259671211027543
dc.identifier.issn2325-9671
dc.identifier.pmcPMC8461131
dc.identifier.pmid34568504
dc.identifier.pubmedURLhttps://pmc.ncbi.nlm.nih.gov/articles/PMC8461131/pdf
dc.identifier.unpaywallURLhttps://doi.org/10.1177/23259671211027543
dc.identifier.urihttps://hdl.handle.net/10668/24557
dc.issue.number9
dc.journal.titleOrthopaedic journal of sports medicine
dc.journal.titleabbreviationOrthop J Sports Med
dc.language.isoen
dc.organizationSAS - Hospital Universitario Regional de Málaga
dc.page.number8
dc.provenanceRealizada la curación de contenido 21/02/2025
dc.publisherSage Publications
dc.pubmedtypeJournal Article
dc.relation.publisherversionhttps://journals.sagepub.com/doi/abs/10.1177/23259671211027543?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectACL
dc.subjectAnterior cruciate ligament
dc.subjectAnteroposterior lengths
dc.subjectArtificial intelligence
dc.subjectBone slope
dc.subjectInjury
dc.subjectMachine learning
dc.subjectMeniscal height
dc.subjectMeniscal slope
dc.subjectPrediction
dc.subjectRisk
dc.subject.decsLesiones del ligamento cruzado anterior
dc.subject.decsCirugía general
dc.subject.decsHeridas y lesiones
dc.subject.decsImagen por resonancia magnética
dc.subject.decsMeniscectomía
dc.subject.decsTraumatismos de la rodilla
dc.subject.meshAnterior Cruciate Ligament
dc.subject.meshAnterior Cruciate Ligament Injuries
dc.subject.meshCross-Sectional Studies
dc.subject.meshMeniscectomy
dc.subject.meshArtificial Intelligence
dc.subject.meshBayes Theorem
dc.titleA Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number9

Files

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
PMC8461131.pdf
Size:
907.76 KB
Format:
Adobe Portable Document Format