Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912306266374144 |
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| author | Hoyer, Gabrielle Gao, Kenneth T Gassert, Felix G Luitjens, Johanna Jiang, Fei Majumdar, Sharmila Pedoia, Valentina |
| author_facet | Hoyer, Gabrielle Gao, Kenneth T Gassert, Felix G Luitjens, Johanna Jiang, Fei Majumdar, Sharmila Pedoia, Valentina |
| contents | This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precision health in osteoarthritis (OA) management and knee replacement (KR) prediction. We combined deep learning-based segmentation of knee joint structures with dimensionality reduction to create an embedded feature space of imaging biomarkers. Through cross-sectional cohort analysis and statistical modeling, we identified specific biomarkers, including variations in cartilage thickness and medial meniscus shape, that are significantly associated with OA incidence and KR outcomes. Integrating these findings into a comprehensive framework represents a considerable step toward personalized knee-joint digital twins, which could enhance therapeutic strategies and inform clinical decision-making in rheumatological care. This versatile and reliable infrastructure has the potential to be extended to broader clinical applications in precision health. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_15396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement Hoyer, Gabrielle Gao, Kenneth T Gassert, Felix G Luitjens, Johanna Jiang, Fei Majumdar, Sharmila Pedoia, Valentina Quantitative Methods Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Applications This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precision health in osteoarthritis (OA) management and knee replacement (KR) prediction. We combined deep learning-based segmentation of knee joint structures with dimensionality reduction to create an embedded feature space of imaging biomarkers. Through cross-sectional cohort analysis and statistical modeling, we identified specific biomarkers, including variations in cartilage thickness and medial meniscus shape, that are significantly associated with OA incidence and KR outcomes. Integrating these findings into a comprehensive framework represents a considerable step toward personalized knee-joint digital twins, which could enhance therapeutic strategies and inform clinical decision-making in rheumatological care. This versatile and reliable infrastructure has the potential to be extended to broader clinical applications in precision health. |
| title | Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for Osteoarthritis and Knee Replacement |
| topic | Quantitative Methods Computer Vision and Pattern Recognition Machine Learning Image and Video Processing Applications |
| url | https://arxiv.org/abs/2501.15396 |