An Automatic Method for Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis
Patron, A., Annala, L., Lainiala, O., Paloneva, J., & Äyrämö, S. (2022). An Automatic Method for Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis. Diagnostics, 12(11), Article 2603. https://doi.org/10.3390/diagnostics12112603
Julkaistu sarjassa
DiagnosticsPäivämäärä
2022Oppiaine
Laskennallinen tiedeComputing, Information Technology and MathematicsHuman and Machine based Intelligence in LearningComputational ScienceComputing, Information Technology and MathematicsHuman and Machine based Intelligence in LearningTekijänoikeudet
© 2022 by the authors. Licensee MDPI, Basel, Switzerland
Efficient and scalable early diagnostic methods for knee osteoarthritis are desired due to the disease’s prevalence. The current automatic methods for detecting osteoarthritis using plain radiographs struggle to identify the subjects with early-stage disease. Tibial spiking has been hypothesized as a feature of early knee osteoarthritis. Previous research has demonstrated an association between knee osteoarthritis and tibial spiking, but the connection to the early-stage disease has not been investigated. We study tibial spiking as a feature of early knee osteoarthritis. Additionally, we develop a deep learning based model for detecting tibial spiking from plain radiographs. We collected and graded 913 knee radiographs for tibial spiking. We conducted two experiments: experiments A and B. In experiment A, we compared the subjects with and without tibial spiking using Mann-Whitney U-test. Experiment B consisted of developing and validating an interpretative deep learning based method for predicting tibial spiking. The subjects with tibial spiking had more severe Kellgren-Lawrence grade, medial joint space narrowing, and osteophyte score in the lateral tibial compartment. The developed method achieved an accuracy of 0.869. We find tibial spiking a promising feature in knee osteoarthritis diagnosis. Furthermore, the detection can be automatized.
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Julkaisija
MDPI AGISSN Hae Julkaisufoorumista
2075-4418Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/159513232
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Lisätietoja rahoituksesta
The work is related to the AI Hub Central Finland project that has received funding from the Council of Tampere Region and European Regional Development Fund and Leverage from the EU 2014-2020. This project has been funded with support from the European Commission. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein. ...Lisenssi
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