Teoh, Yun Xin and Othmani, Alice and Lai, Khin Wee and Goh, Siew Li and Usman, Juliana (2023) Stratifying knee osteoarthritis features through multitask deep hybrid learning: Data from the osteoarthritis initiative. Computer Methods and Programs in Biomedicine, 242. ISSN 0169-2607, DOI https://doi.org/10.1016/j.cmpb.2023.107807.
Full text not available from this repository.Abstract
Background and objective: Knee osteoarthritis (OA) is a debilitating musculoskeletal disorder that causes functional disability. Automatic knee OA diagnosis has great potential of enabling timely and early intervention, that can potentially reverse the degenerative process of knee OA. Yet, it is a tedious task, concerning the heterogeneity of the disorder. Most of the proposed techniques demonstrated single OA diagnostic task widely based on Kellgren Lawrence (KL) standard, a composite score of only a few imaging features (i.e. osteophytes, joint space narrowing and subchondral bone changes). However, only one key disease pattern was tackled. The KL standard fails to represent disease pattern of individual OA features, particularly osteophytes, joint-space narrowing, and pain intensity that play a fundamental role in OA manifestation. In this study, we aim to develop a multitask model using convolutional neural network (CNN) feature extractors and machine learning classifiers to detect nine important OA features: KL grade, knee osteophytes (both knee, medial fibular: OSFM, medial tibial: OSTM, lateral fibular: OSFL, and lateral tibial: OSTL), joint-space narrowing (medial: JSM, and lateral: JSL), and patient-reported pain intensity from plain radiography. Methods: We proposed a new feature extraction method by replacing fully-connected layer with global average pooling (GAP) layer. A comparative analysis was conducted to compare the efficacy of 16 different convolutional neural network (CNN) feature extractors and three machine learning classifiers. Results: Experimental results revealed the potential of CNN feature extractors in conducting multitask diagnosis. Optimal model consisted of VGG16-GAP feature extractor and KNN classifier. This model not only outperformed the other tested models, it also outperformed the state-of-art methods with higher balanced accuracy, higher Cohen's kappa, higher F1, and lower mean squared error (MSE) in seven OA features prediction. Conclusions: The proposed model demonstrates pain prediction on plain radiographs, as well as eight OA-related bony features. Future work should focus on exploring additional potential radiological manifestations of OA and their relation to therapeutic interventions.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Education, Malaysia (FRGS/1/2022/SKK01/UM/02/1), Malaysia-France University Centre (MFUC) |
| Uncontrolled Keywords: | Deep hybrid learning; Computer-aided diagnosis; Joint-space narrowing; Knee osteoarthritis; Knee pain; Osteophytes |
| Subjects: | G Geography. Anthropology. Recreation > GV Recreation Leisure R Medicine > R Medicine (General) |
| Divisions: | Faculty of Engineering > Department of Biomedical Engineering Faculty of Medicine Faculty of Medicine > Sport Medicine Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 08 Sep 2025 02:08 |
| Last Modified: | 08 Sep 2025 02:08 |
| URI: | http://eprints.um.edu.my/id/eprint/50635 |
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