A1 Journal article (refereed)
An Automatic Method for Assessing Spiking of Tibial Tubercles Associated with Knee Osteoarthritis (2022)

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

JYU authors or editors

Publication details

All authors or editors: Patron, Anri; Annala, Leevi; Lainiala, Olli; Paloneva, Juha; Äyrämö,Sami

Journal or series: Diagnostics

eISSN: 2075-4418

Publication year: 2022

Publication date: 27/10/2022

Volume: 12

Issue number: 11

Article number: 2603

Publisher: MDPI AG

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.3390/diagnostics12112603

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/83892

Additional information: Data available in a publicly accessible repository that does not issue DOIs Publicly available datasets were analyzed in this study. OAI data can be found here: https://nda.nih.gov/oai/ (accessed on 4 October 2022). MOST data was available at: https://most.ucsf.edu (currently unavailable as of 4 October 2022).

Abstract

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.

Keywords: musculoskeletal diseases; arthrosis; knees; tibia; diagnostics; x-ray examination; machine learning; deep learning; neural networks (information technology)

Free keywords: tibial spiking; convolutional neural networks

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2022

JUFO rating: 1