Multiclass Support Vector Machine improves the Pivot-shift grading from Gerdy's acceleration resultant prior to the acute Anterior Cruciate Ligament surgery

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dc.contributor.authorYanez-Diaz, Roberto
dc.contributor.authorRoby, Matias
dc.contributor.authorSilvestre, Rony
dc.contributor.authorZamorano, Hector
dc.contributor.authorVergara, Francisco
dc.contributor.authorSandoval, Carlos
dc.contributor.authorNeira, Alejandro
dc.contributor.authorYanez-Rojo, Cristobal
dc.contributor.authorDe la Fuente, Carlos
dc.date.accessioned2025-01-20T20:10:31Z
dc.date.available2025-01-20T20:10:31Z
dc.date.issued2023
dc.description.abstractIntroduction: Rotatory laxity acceleration still lacks objective classification due to interval grading superposition, resulting in a biased pivot shift grading prior to the Anterior Cruciate Ligament (ACL) reconstruction. However, data analysis might help improve data grading in the operative room. Therefore, we described the improvement of the pivot-shift categorization in Gerdy's acceleration under anesthesia prior to ACL surgery using a support vector machine (SVM) classification, surgeon, and literature reference. Methods: Seventy-five patients (aged 30.3 +/- 10.2 years, and IKDC 52.0 +/- 16.5 points) with acute ACL rupture under anesthesia prior to ACL surgery were analyzed. Patients were graded with pivot-shift sign glide ( + ), clunk ( ++ ), and ( +++ ) gross by senior orthopedic surgeons. At the same time, the tri-axial tibial plateau acceleration was measured. Categorical data were statistically described, and the accelerometry and categorical data were associated ( alpha = 5%). A multiclass SVM kernel with the best accuracy trained by orthopedic surgeons and assisted from literature for missing data was compared with experienced surgeons and literature interval grading. The cubic SVM classifier achieved the best grading.Results: The intra-group proportions were different for each grading in the three compared strategies ( p < 0.001). The inter-group proportions were different for all comparisons ( p < 0.001). There were significant ( p < 0.001) associations (Tau: 0.69, -0.28, and -0.50) between the surgeon and SVM, the surgeon and interval grading, and the interval and SVM, respectively.Conclusion: The multiclass SVM classifier improves the acceleration categorization of the ( + ), ( ++ ), and ( +++ ) pivot shift sign prior to the ACL surgery in agreement with surgeon criteria.(c) 2023 Elsevier Ltd. All rights reserved.
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.injury.2023.03.020
dc.identifier.eissn1879-0267
dc.identifier.issn0020-1383
dc.identifier.urihttps://doi.org/10.1016/j.injury.2023.03.020
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/92076
dc.identifier.wosidWOS:001002644100001
dc.issue.numero6
dc.language.isoen
dc.pagina.final1774
dc.pagina.inicio1770
dc.revistaInjury-international journal of the care of the injured
dc.rightsacceso restringido
dc.subjectPivot shift
dc.subjectMachine learning
dc.subjectRotatory instability
dc.subjectLigament ruptures
dc.titleMulticlass Support Vector Machine improves the Pivot-shift grading from Gerdy's acceleration resultant prior to the acute Anterior Cruciate Ligament surgery
dc.typeartículo
dc.volumen54
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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