A3 Book section, Chapters in research books
Validation of Knee KL-classifying Deep Neural Network with Finnish Patient Data (2022)


Niinimäki, E., Paloneva, J., Pölönen, I., Heinonen, A., & Äyrämö, S. (2022). Validation of Knee KL-classifying Deep Neural Network with Finnish Patient Data. In T. T. Tuovinen, J. Periaux, & P. Neittaanmäki (Eds.), Computational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges (pp. 177-188). Springer. Intelligent Systems, Control and Automation: Science and Engineering, 76. https://doi.org/10.1007/978-3-030-70787-3_12


JYU authors or editors


Publication details

All authors or editorsNiinimäki, Esko; Paloneva, Juha; Pölönen, Ilkka; Heinonen, Ari; Äyrämö, Sami

Parent publicationComputational Sciences and Artificial Intelligence in Industry : New Digital Technologies for Solving Future Societal and Economical Challenges

Parent publication editorsTuovinen, Tero T.; Periaux, Jacques; Neittaanmäki, Pekka

ISBN978-3-030-70786-6

eISBN978-3-030-70787-3

Journal or seriesIntelligent Systems, Control and Automation: Science and Engineering

ISSN2213-8986

eISSN2213-8994

Publication year2022

Number in series76

Pages range177-188

Number of pages in the book275

PublisherSpringer

Place of PublicationCham

Publication countrySwitzerland

Publication languageEnglish

DOIhttps://doi.org/10.1007/978-3-030-70787-3_12

Publication open accessNot open

Publication channel open access

Additional informationThe CSAI 2019 Conference (Computational Science and AI in Industry: New Digital Technologies for Solving Future Societal and Economical Challenges) took place at Jyväskylä, Finland, on June 12–14, 2019.


Abstract

Osteoarthritis (OA) is the most common form of joint disease in the world. The diagnosis of OA is currently made by human experts and suffers from subjectivity, but recently new promising detection algorithms have been developed. We validated the current state-of-the-art KL-classifying neural network model for knee OA using knee X-rays taken from postmenopausal women suffering from knee pain attributable to OA. The performance of the model on the clinical data was considerably lower compared to the previous results on population-based test data. This suggests that the performance of the current grading methods is not yet adequate to be applied in clinical settings. The present results also emphasize the importance of using clinical data for performance evaluation before deploying medical machine learning models.


Keywordsarthrosiskneesdiagnosticsx-ray examinationmachine learningneural networks (information technology)validation

Free keywordsdeep learning; neural network; convolution; osteoarthritis; validation


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating2


Last updated on 2024-30-04 at 18:16