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

•Machine learning can help the pivot shift grading.•Multiclass Support Vector Machine improves the pivot shift grading.•Multiclass Support Vector Machine can assist against surgeon doubts classification. Rotatory laxity acceleration still lacks objective classification due to interval grading superp...

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Bibliographic Details
Published in:Injury 2023-06, Vol.54 (6), p.1770-1774
Main Authors: Yañez-Diaz, Roberto, Roby, Matías, Silvestre, Rony, Zamorano, Héctor, Vergara, Francisco, Sandoval, Carlos, Neira, Alejandro, Yañez-Rojo, Cristóbal, De la Fuente, Carlos
Format: Article
Language:English
Subjects:
Ligament ruptures
Machine learning
Pivot shift
Rotatory instability
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Summary:•Machine learning can help the pivot shift grading.•Multiclass Support Vector Machine improves the pivot shift grading.•Multiclass Support Vector Machine can assist against surgeon doubts classification. 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. 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. The intra-group proportions were different for each grading in the three compared strategies (p 
ISSN:0020-1383
1879-0267
DOI:10.1016/j.injury.2023.03.020