Stratifying knee osteoarthritis features through multitask deep hybrid learning: Data from the osteoarthritis initiative.

Autor: Teoh YX; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia; LISSI, Université Paris-Est Créteil, Vitry sur Seine, 94400, France., Othmani A; LISSI, Université Paris-Est Créteil, Vitry sur Seine, 94400, France. Electronic address: alice.othmani@u-pec.fr., Lai KW; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia. Electronic address: lai.khinwee@um.edu.my., Goh SL; Sports Medicine Unit, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, 50603, Malaysia; Centre for Epidemiology and Evidence-Based Practice, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, 50603, Malaysia., Usman J; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Jazyk: angličtina
Zdroj: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2023 Dec; Vol. 242, pp. 107807. Date of Electronic Publication: 2023 Sep 20.
DOI: 10.1016/j.cmpb.2023.107807
Abstrakt: 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.
Competing Interests: Declaration of Competing Interest We confirm that this work is original, has not been published elsewhere, and is not currently being considered for publication elsewhere. The consent of all authors of this paper has been obtained for submitting the paper and all authors declare no conflict of interest.
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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