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

Roberto Yañez-Diaz, Matías Roby, Rony Silvestre, Héctor Zamorano, Francisco Vergara, Carlos Sandoval, Alejandro Neira, Cristóbal Yañez-Rojo, Carlos De la Fuente

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: 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 (α = 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.

Original languageEnglish
Pages (from-to)1770-1774
Number of pages5
JournalInjury
Volume54
Issue number6
DOIs
Publication statusPublished - Jun 2023
Externally publishedYes

Keywords

  • Ligament ruptures
  • Machine learning
  • Pivot shift
  • Rotatory instability

ASJC Scopus subject areas

  • Emergency Medicine
  • Orthopedics and Sports Medicine

Fingerprint

Dive into the research topics of 'Multiclass Support Vector Machine improves the Pivot-shift grading from Gerdy's acceleration resultant prior to the acute Anterior Cruciate Ligament surgery'. Together they form a unique fingerprint.

Cite this