Publications in Scientific Journals:

J. Livija, S. Vitaly, J. Hladuvka, M. Sergey, A. Aziz, W. Krzanowski:
"Deep learning for early detection of pathological changes in X-ray bone microstructures: case of osteoarthritis";
Nature, Sci Rep 11, 2294 (2021) (2021), 27.01.2021; 2294 - 2323.

English abstract:
Texture features are designed to quantitatively evaluate patterns of spatial distribution of image pixels for purposes of image analysis and interpretation. Unexplained variations in the texture patterns often lead to misinterpretation and undesirable consequences in medical image analysis. In this paper we explore the ability of machine learning (ML) methods to design a radiology test of Osteoarthritis (OA) at early stage when the number of patients´ cases is small. In our experiments we use high-resolution X-ray images of knees in patients which were identified with Kellgren-Lawrence scores progressing from 1. The existing ML methods have provided a limited diagnostic accuracy, whilst the proposed Group Method of Data Handling strategy of Deep Learning has significantly extended the diagnostic test. The comparative experiments demonstrate that the proposed framework using the Zernike-based texture features has significantly improved the diagnostic accuracy on average by 11%. This allows us to conclude that the designed model for early diagnostic of OA will provide more accurate radiology tests, although new study is required when a large number of patients´ cases will be available.

Mathematics and computing , Osteoarthritis

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

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Created from the Publication Database of the Vienna University of Technology.