Evidence map›Paper›PMID 39984725›Full record

ArticleNPJ digital medicine2025

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

Gabrielle Hoyer, Kenneth T Gao, Felix G Gassert, Johanna Luitjens, Fei Jiang, Sharmila Majumdar, Valentina Pedoia

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Gabrielle HoyerDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA. gabbie.hoyer@ucsf.edu.
Kenneth T GaoDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Felix G GassertDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Johanna LuitjensDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Fei JiangDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Sharmila MajumdarDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
Valentina PedoiaDepartment of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.

Funding

Multidimensional MRI-based Non-Euclidean Deep Learning to Study OsteoarthritisR00AR070902 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PEDOIA, VALENTINA · 2018 to 2020
$747k
National Institute of Arthritis and Skin Diseases NIH/NIAMS R33AR073552/R00AR070902NIAMS NIH HHS R00 AR070902
6 · The paper itself

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.

Identifiers

PMID39984725
PMCPMC11845592

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.