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

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

doi:10.1016/j.injury.2023.03.020

Cited by 1 publication

(2 citation statements)

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“…Most authors that approach the quantification of the pivot test through accelerometers seem to agree on a method to interpret a signal acquired by the pivot test with only one morphological characteristic in the graph, which allows them to detect subluxation and reduction of the joint, equivalent to the “pivot” segment of the signals acquired in this study. Nevertheless, this method could be enhanced by the use of machine learning algorithms [ 40 , 41 , 42 , 43 , 44 , 45 ]. Labbe et al, and more recently Yañez-Diaz et al, implemented SVM algorithms to signals acquired with accelerometers in order to grade the PS phenomenon objectively, reporting acceptable results, which can be compared to the results of the algorithm to organize the signals by class.…”

Section: Discussionmentioning

confidence: 99%

“…Labbe et al, and more recently Yañez-Diaz et al, implemented SVM algorithms to signals acquired with accelerometers in order to grade the PS phenomenon objectively, reporting acceptable results, which can be compared to the results of the algorithm to organize the signals by class. Notwithstanding, the SVM was not the best method to sort the signals by grade [ 44 , 45 ].…”

Section: Discussionmentioning

confidence: 99%

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Mobile App for Enhanced Anterior Cruciate Ligament (ACL) Assessment in Conscious Subjects: “Pivot-Shift Meter”

Berumen-Nafarrate,

Ramos-Moctezuma,

Sigala-González

et al. 2024

JPM

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Anterior cruciate ligament (ACL) instability poses a considerable challenge in traumatology and orthopedic medicine, demanding precise diagnostics for optimal treatment. The pivot-shift test, a pivotal assessment tool, relies on subjective interpretation, emphasizing the need for supplementary imaging. This study addresses this limitation by introducing a machine learning classification algorithm integrated into a mobile application, leveraging smartphones’ built-in inertial sensors for dynamic rotational stability assessment during knee examinations. Orthopedic specialists conducted knee evaluations on a cohort of 52 subjects, yielding valuable insights. Quantitative analyses, employing the Intraclass Correlation Coefficient (ICC), demonstrated robust agreement in both intraobserver and interobserver assessments. Specifically, ICC values of 0.94 reflected strong concordance in the timing between maneuvers, while signal amplitude exhibited consistency, with the ICC ranging from 0.71 to 0.66. The introduced machine learning algorithms proved effective, accurately classifying 90% of cases exhibiting joint hypermobility. These quantifiable results underscore the algorithm’s reliability in assessing knee stability. This study emphasizes the practicality and effectiveness of implementing machine learning algorithms within a mobile application, showcasing its potential as a valuable tool for categorizing signals captured by smartphone inertial sensors during the pivot-shift test.

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