Mohafez, Hamidreza and Sayed, Hamza and Hadizadeh, Maryam and Wee, Lai Khin and Ahmad, Siti Anom (2022) Detection of knee osteoarthritis and prediction of its severity using x-ray image analysis and patients assessment data: a hybrid design. In: 6th Kuala Lumpur International Conference on Biomedical Engineering, BioMed 2021.
Full text not available from this repository.Abstract
Knee osteoarthritis (KOA) is a degenerative disease associated with cartilage loss, causes limitations in the range of movement, and known to be one of the most disabling age-associated diseases around the world. It is vital to predict its presence and severity at early stage to tailor the interventions and treatments properly. Traditionally, X-ray Images are graded by radiologists to quantify KOA severity; however, this approach suffers from high levels of subjectivity due to the semi-quantitative nature of grading systems. Numerous attempts have been made to recruit automated X-ray image analysis to quantify KOA severity, but few studies have used pertinent assessment data such as symptoms and medications being used to establish accurate predictive model. So, we proposed a statistical model built on combination of features extracted from X-ray images and patients’ data using ordinal regression analysis. The results revealed that the developed model based on combination of KOA X-ray key features and patient assessment data is able to predict the severity of KOA with high level of accuracy (89.2) and acceptable level of inter-rater reliability with quadratic weighted Cohen’s Kappa coefficient (QWK) of 0.8337. The study outcomes suggested that variables showing impaired knee functions are the best indicators to quantify knee OA presence and severity that may be used in conjunction with X-ray biomarkers for developing intervention and targeted treatment. © 2022, Springer Nature Switzerland AG.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Forecasting; Grading; Regression analysis; Early detection; Hybrid design; Image-analysis; Joint space width; Kellgren-lawrence; Knee osteoarthritis; Model development; Patient assessment data; X-ray image; X-ray image analyse; Image analysis |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department Faculty of Sports and Exercise Science (formerly known as Centre for Sports & Exercise Sciences) |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 07 Nov 2023 01:49 |
| Last Modified: | 07 Nov 2023 01:49 |
| URI: | http://eprints.um.edu.my/id/eprint/43381 |
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