title: Objective grading of the pivot shift phenomenon using a support vector machine approach.
abstract: The pivot shift test is the only clinical test that has been shown to correlate with subjective criteria of knee joint function following rupture of the anterior cruciate ligament. The grade of the pivot shift is important in predicting short- and long-term outcome. However, because this grade is established by a clinician in a subjective manner, the pivot shift's value as a clinical tool is reduced. The purpose of this study was to develop a system that will objectively grade the pivot shift test based on recorded knee joint kinematics. Fifty-six subjects with different degrees of knee joint stability had the pivot shift test performed by one of eight different orthopaedic surgeons while their knee joint kinematics were recorded. A support vector machine based algorithm was used to objectively classify these recordings according to a clinical grade. The grades established by the surgeons were used as the gold standard for the development of the classifier. There was substantial agreement between our classifier and the surgeons in establishing the grade (weighted kappa=0.68). Seventy-one of 107 recordings (66%) were given the same grade and 96% of the time our classifier was within one grade of that given by the surgeons. Moreover, grades 0 and 1 were distinguished from grade 2 to 3 with 86% sensitivity and 90% specificity. Our results show the feasibility of automatically grading the pivot shift in a manner similar to that of an experienced clinician, based on knee joint kinematics.
authors.affiliation: Laboratoire de recherche en imagerie et orthopédie, Centre de recherche, Centre hospitalier de l'Université de Montréal (CHUM), Montréal, Canada. dlabbe@gmail.com
authors.forename: David R
authors.initials: DR
authors.lastname: Labbe
authors.forename: Jacques A
authors.initials: JA
authors.lastname: de Guise
authors.forename: Neila
authors.initials: N
authors.lastname: Mezghani
authors.forename: Véronique
authors.initials: V
authors.lastname: Godbout
authors.forename: Guy
authors.initials: G
authors.lastname: Grimard
authors.forename: David
authors.initials: D
authors.lastname: Baillargeon
authors.forename: Patrick
authors.initials: P
authors.lastname: Lavigne
authors.forename: Julio
authors.initials: J
authors.lastname: Fernandes
authors.forename: Pierre
authors.initials: P
authors.lastname: Ranger
authors.forename: Nicola
authors.initials: N
authors.lastname: Hagemeister
mesh_terms: D000328:Adult; D000465:Algorithms; D000070598:Anterior Cruciate Ligament Injuries; D001185:Artificial Intelligence; D001696:Biomechanical Phenomena; D005260:Female; D006801:Humans; D007593:Joint Instability / Q000503:physiopathology; D007718:Knee Injuries / Q000503:physiopathology; D007719:Knee Joint / Q000502:physiology* / Q000503:physiopathology; D008297:Male; D008954:Models, Biological; D016059:Range of Motion, Articular / Q000502:physiology; D055815:Young Adult