Statistical shape modelling reveals large and distinct subchondral bony differences in osteoarthritic knees |
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| Institution: | 1. Research School of Electrical, Energy and Materials Engineering, Australian National University, Ian Ross Building 31, North Road, Acton, ACT, 2601;2. The Australian National University, Acton, ACT, 2601, Australia;3. University of New South Wales Canberra at ADFA, PO Box 7916, Canberra BC, ACT 2610, Australia;4. Faculty of Health, University of Canberra, Locked Bag 1, 2601, Australia;5. Trauma and Orthopaedic Research Unit, Canberra Hospital. Woden, ACT, 2606, Australia;1. Department of Mechanics, BioMech, University College Ghent, Valentin Vaerwijckweg 1, 9000 Ghent, Belgium;2. Department of Production and Construction, Ghent University, 9052 Zwijnaarde, Belgium;3. Department of Physical medicine and Orthopaedic Surgery, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium;4. Department of Civil Engineering, IBiTech — bioMMeda, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium;5. Department of Computer Science, University of Southern California, USA;6. Monica Orthopaedic Research Institute (MORE Institute), 2100 Antwerp, Belgium;7. Monica Hospital, 2100 Antwerp, Belgium |
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| Abstract: | Knee osteoarthritis (OA) results in changes such as joint-space narrowing and osteophyte formation. Radiographic classification systems group patients by the presence or absence of these gross anatomical features but are poorly correlated to function. Statistical-shape modelling (SSM) can detect subtle differences in 3D-bone geometry, providing an opportunity for accurate predictive models. The aim of this study was to describe and quantify the main modes of shape variation which distinguish end-stage OA from asymptomatic knees. Seventy-six patients with OA and 77 control participants received a CT of their knee. 3D models of the joint were created by manual segmentation. A template mesh was fitted to all meshes and rigidly aligned resulting in a set of correspondent meshes. Principal Component Analysis (PCA) was performed to create the SSM. Logistic regression was performed on the PCA weights to distinguish morphological features of the two groups. The first 7 modes of the SSM captured >90% shape variation with 6 modes best distinguishing between OA and asymptomatic knees. OA knees displayed sub-chondral bone expansion particularly in the condyles and posterior medial tibial plateau of up to 10 mm. The model classified the two groups with 95% accuracy, 96% sensitivity, 94% specificity, and 97% AUC. There were distinct features which differentiated OA from asymptomatic knees. Further research will elucidate how magnitude and location of shape changes in the knee influence clinical and functional outcomes. |
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| Keywords: | Osteoarthritis Knee Statistical shape model |
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