Liukkonen, M. K., Mononen, M. E., Klets, O., Arokoski, J. P., Saarakkala, S., & Korhonen, R. K. (2017). Simulation of Subject-Specific Progression of Knee Osteoarthritis and Comparison to Experimental Follow-up Data: Data from the Osteoarthritis Initiative. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-09013-7
Simulation of subject-specific progression of knee osteoarthritis and comparison to experimental follow-up data : data from the osteoarthritis initiative
|Author:||Liukkonen, Mimmi K.1; Mononen, Mika E.1; Klets, Olesya2,3;|
1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
2Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
3Medical Research Center, University of Oulu and Oulu University Hospital, Oulu, Finland
4Department of Physical and Rehabilitation Medicine, Helsinki University Hospital, Helsinki, Finland
5University of Helsinki, Helsinki, Finland
6Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
7Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland
|Online Access:||PDF Full Text (PDF, 4.6 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019081223929
|Publish Date:|| 2019-08-12
Economic costs of osteoarthritis (OA) are considerable. However, there are no clinical tools to predict the progression of OA or guide patients to a correct treatment for preventing OA. We tested the ability of our cartilage degeneration algorithm to predict the subject-specific development of OA and separate groups with different OA levels. The algorithm was able to predict OA progression similarly with the experimental follow-up data and separate subjects with radiographical OA (Kellgren-Lawrence (KL) grade 2 and 3) from healthy subjects (KL0). Maximum degeneration and degenerated volumes within cartilage were significantly higher (p < 0.05) in OA compared to healthy subjects, KL3 group showing the highest degeneration values. Presented algorithm shows a great potential to predict subject-specific progression of knee OA and has a clinical potential by simulating the effect of interventions on the progression of OA, thus helping decision making in an attempt to delay or prevent further OA symptoms.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3126 Surgery, anesthesiology, intensive care, radiology
The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. 281180, the Academy of Finland (grants 286526, 268378, 305138), Sigrid Juselius Foundation, the strategic fundings of the University of Eastern Finland and University of Oulu, Kuopio University Hospital (VTR grant 5041752, PY210), MRC-Oulu (Medical Research Centre) grant and Doctoral Programme in Science, Technology and Computing, University of Eastern Finland. CSC–IT Center for Science Ltd, Finland is acknowledged for providing the finite-element modelling software. Mélody Guillaud and Ville-Veikko Tiili are acknowledged for segmenting and creating FE meshes from OAI database. Mika Nevalainen is acknowledged for MOAKS scoring of the joints. A trained statistician Santtu Mikkonen from the University of Eastern Finland is acknowledged for helping with statistical analyses.
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