Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement

Abstract 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 s...

Full description

Saved in:
Bibliographic Details
Main Authors: Gabrielle Hoyer, Kenneth T. Gao, Felix G. Gassert, Johanna Luitjens, Fei Jiang, Sharmila Majumdar, Valentina Pedoia
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-025-01507-3
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1850190829096796160
author Gabrielle Hoyer
Kenneth T. Gao
Felix G. Gassert
Johanna Luitjens
Fei Jiang
Sharmila Majumdar
Valentina Pedoia
author_facet Gabrielle Hoyer
Kenneth T. Gao
Felix G. Gassert
Johanna Luitjens
Fei Jiang
Sharmila Majumdar
Valentina Pedoia
author_sort Gabrielle Hoyer
collection DOAJ
description Abstract 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 Article
id doaj-art-ca18cb841f364d39a35e55da0502ab09
institution OA Journals
issn 2398-6352
language English
publishDate 2025-02-01
publisher Nature Portfolio
record_format Article
series npj Digital Medicine
spelling doaj-art-ca18cb841f364d39a35e55da0502ab092025-08-20T02:15:08ZengNature Portfolionpj Digital Medicine2398-63522025-02-018111510.1038/s41746-025-01507-3Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacementGabrielle Hoyer0Kenneth T. Gao1Felix G. Gassert2Johanna Luitjens3Fei Jiang4Sharmila Majumdar5Valentina Pedoia6Department of Radiology and Biomedical Imaging, University of California San FranciscoDepartment of Radiology and Biomedical Imaging, University of California San FranciscoDepartment of Radiology and Biomedical Imaging, University of California San FranciscoDepartment of Radiology and Biomedical Imaging, University of California San FranciscoDepartment of Epidemiology and Biostatistics, University of California San FranciscoDepartment of Radiology and Biomedical Imaging, University of California San FranciscoDepartment of Radiology and Biomedical Imaging, University of California San FranciscoAbstract 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.https://doi.org/10.1038/s41746-025-01507-3
spellingShingle Gabrielle Hoyer
Kenneth T. Gao
Felix G. Gassert
Johanna Luitjens
Fei Jiang
Sharmila Majumdar
Valentina Pedoia
Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement
npj Digital Medicine
title Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement
title_full Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement
title_fullStr Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement
title_full_unstemmed Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement
title_short Foundations of a knee joint digital twin from qMRI biomarkers for osteoarthritis and knee replacement
title_sort foundations of a knee joint digital twin from qmri biomarkers for osteoarthritis and knee replacement
url https://doi.org/10.1038/s41746-025-01507-3
work_keys_str_mv AT gabriellehoyer foundationsofakneejointdigitaltwinfromqmribiomarkersforosteoarthritisandkneereplacement
AT kennethtgao foundationsofakneejointdigitaltwinfromqmribiomarkersforosteoarthritisandkneereplacement
AT felixggassert foundationsofakneejointdigitaltwinfromqmribiomarkersforosteoarthritisandkneereplacement
AT johannaluitjens foundationsofakneejointdigitaltwinfromqmribiomarkersforosteoarthritisandkneereplacement
AT feijiang foundationsofakneejointdigitaltwinfromqmribiomarkersforosteoarthritisandkneereplacement
AT sharmilamajumdar foundationsofakneejointdigitaltwinfromqmribiomarkersforosteoarthritisandkneereplacement
AT valentinapedoia foundationsofakneejointdigitaltwinfromqmribiomarkersforosteoarthritisandkneereplacement