Universität zu Köln

Abedin, Jaynal ORCID: 0000-0002-4830-4092, Antony, Joseph, McGuinness, Kevin ORCID: 0000-0003-1336-6477, Moran, Kieran ORCID: 0000-0003-2015-8967, O'Connor, Noel E., Rebholz-Schuhmann, Dietrich and Newell, John (2019). Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images. Sci Rep, 9. LONDON: NATURE PUBLISHING GROUP. ISSN 2045-2322

Full text not available from this repository.

Abstract

Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments according to the Kellgren and Lawrence grading scheme; a five-point ordinal scale (0-4). In this study, we used Elastic Net (EN) and Random Forests (RF) to build predictive models using patient assessment data (i.e. signs and symptoms of both knees and medication use) and a convolution neural network (CNN) trained using X-ray images only. Linear mixed effect models (LMM) were used to model the within subject correlation between the two knees. The root mean squared error for the CNN, EN, and RF models was 0.77, 0.97 and 0.94 respectively. The LMM shows similar overall prediction accuracy as the EN regression but correctly accounted for the hierarchical structure of the data resulting in more reliable inference. Useful explanatory variables were identified that could be used for patient monitoring before X-ray imaging. Our analyses suggest that the models trained for predicting the KOA severity levels achieve comparable results when modeling X-ray images and patient data. The subjectivity in the KL grade is still a primary concern.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Abedin, Jaynal UNSPECIFIED orcid.org/0000-0002-4830-4092 UNSPECIFIED Antony, Joseph UNSPECIFIED UNSPECIFIED UNSPECIFIED McGuinness, Kevin UNSPECIFIED orcid.org/0000-0003-1336-6477 UNSPECIFIED Moran, Kieran UNSPECIFIED orcid.org/0000-0003-2015-8967 UNSPECIFIED O'Connor, Noel E. UNSPECIFIED UNSPECIFIED UNSPECIFIED Rebholz-Schuhmann, Dietrich UNSPECIFIED UNSPECIFIED UNSPECIFIED Newell, John UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-150911
DOI:	10.1038/s41598-019-42215-9
Journal or Publication Title:	Sci Rep
Volume:	9
Date:	2019
Publisher:	NATURE PUBLISHING GROUP
Place of Publication:	LONDON
ISSN:	2045-2322
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language OPPORTUNITIES; REGRESSION; VALIDITY; KELLGREN Multiple languages Multidisciplinary Sciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/15091