Abstract
Knee kinematic data consist of a small sample of high-dimensional vectors recording repeated measurements of the temporal variation of each of the three fundamental angles of knee three-dimensional rotation during a walking cycle. In applications such as knee pathology classification, the notorious problems of high-dimensionality (the curse of dimensionality), high intra-class variability, and inter-class similarity make this data generally difficult to interpret. In the face of these difficulties, the purpose of this study is to investigate knee kinematic data classification by a Kohonen neural network generalized to encode samples of multidimensional data vectors rather than single such vectors as in the standard network. The network training algorithm and its ensuing classification function both use the Hotelling T 2 statistic to evaluate the underlying sample similarity, thus affording efficient use of training data for network development and robust classification of observed data. Applied to knee osteoarthritis pathology discrimination, namely the femoro-rotulian (FR) and femoro-tibial (FT) categories, the scheme improves on the state-of-the-art methods.
Highlights
Frequent knee pain affects approximately one adult in four, limiting function and diminishing quality of life
Several studies have addressed the problem of distinguishing asymptomatic and OA groups [3,4,5,6,7] and assessing the severity of the OA disease according to the Kellgren Lawrence (KL) score [8]
We investigate a Kohonen neural network generalized to encode data in the form of samples, which we apply to knee OA pathology categorization using knee kinematic data samples
Summary
Frequent knee pain affects approximately one adult in four, limiting function and diminishing quality of life. Knee pain in people 50 years or older is predominantly caused by osteoarthritis (OA). It is a major reason for knee replacements among knee osteoarthritis patients in general [1,2]. This severe impact on human health and the soaring financial cost justify the recent accrued research interest in computer-aided, objective knee disease diagnosis methods. Such methods would facilitate diagnosis and improve its accuracy so that the disease can be treated more effectively. None has considered distinguishing two classes of knee OA pathologies, namely femero-rotulian (FR) and femero-tibilal (FT), or further consider, in addition to FR and FT, category FR-FT representing the incidence of both diseases FR and FT in a same individual
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