ArticleHeliyon2023
A joint multi-modal learning method for early-stage knee osteoarthritis disease classification.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- [Advances in deep learning multimodal fusion for early diagnosis of knee osteoarthritis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Chromatic differential confocal matrix-based 3D topography and hyperspectral imaging with deep learning for osteoarthritis grading.Biomedical optics express · 2026Article
- Explainable multimodal knee osteoarthritis diagnosis from X-ray images using anomaly detection with clinical and structural biomarkers.Scientific reports · 2026Article
- Interpretable machine learning integrates multi-source biomarkers for osteoarthritis diagnosis and mechanistic insights: A temporomandibular joint model.Osteoarthritis and cartilage · 2025Article
- DIKOApp: An AI-Based Diagnostic System for Knee Osteoarthritis.Journal of imaging informatics in medicine · 2025Article
- Enhancing Human Activity Recognition through Integrated Multimodal Analysis: A Focus on RGB Imaging, Skeletal Tracking, and Pose Estimation.Sensors (Basel, Switzerland) · 2024Article
- Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting From Multimodal Data.IEEE transactions on medical imaging · 2024Article
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Authors and funding
7 authors.
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Abstract
Osteoarthritis (OA) is a progressive and chronic disease. Identifying the early stages of OA disease is important for the treatment and care of patients. However, most state-of-the-art methods only use single-modal data to predict disease status, so that these methods usually ignore complementary information in multi-modal data. In this study, we develop an integrated multi-modal learning method (MMLM) that uses an interpretable strategy to select and fuse clinical, imaging, and demographic features to classify the grade of early-stage knee OA disease. MMLM applies XGboost and ResNet50 to extract two heterogeneous features from the clinical data and imaging data, respectively. And then we integrate these extracted features with demographic data. To avoid the negative effects of redundant features in a direct integration of multiple features, we propose a L1-norm-based optimization method (MMLM) to regularize the inter-correlations among the multiple features. MMLM was assessed using the Osteoarthritis Initiative (OAI) data set with machine learning classifiers. Extensive experiments demonstrate that MMLM improves the performance of the classifiers. Furthermore, a visual analysis of the important features in the multimodal data verified the relations among the modalities when classifying the grade of knee OA disease.
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