Evidence map›Paper›PMID 41876760›Full record

ArticleNPJ digital medicine2026

Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes.

Gabrielle Hoyer, Michelle W Tong, Rupsa Bhattacharjee, Valentina Pedoia, Sharmila Majumdar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Gabrielle HoyerCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA. gabbie.hoyer@ucsf.edu.
Michelle W TongCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
Rupsa BhattacharjeeCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
Valentina PedoiaCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
Sharmila MajumdarCenter for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.

Funding

Structural, Biochemical and Functional Connectivity in Osteoarthritis using Quantitative Magnetic Resonance Imaging and Skeletal BiomechanicsR01AR069006 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA, SOUZA, RICHARD BRIAN · 2016 to 2025
$7.2M
MRI TO STUDY CARTILAGE/BONE INTERACTIONSR01AR046905 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA · 1999 to 2015
$6.3M
Translation of Quantitative Imaging in OsteoarthritisP50AR060752 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SOUZA, RICHARD BRIAN · 2011 to 2015
$6.2M
Technology Research Site for Advanced, Faster Quantitative Imaging for BACPACUH3AR076724 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA · 2020 to 2023
$4.8M
Understanding the mechanisms by which weight change affects progression of knee osteoarthritis in obese and overweight individuals: An analysis of the Osteoarthritis Initiative DatasetR01AR078917 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GABRIELLE JOSEPH, THOMAS M LINK · 2021 to 2026
$3.4M
Ultra-Fast Knee MRI with Deep LearningR01AR078762 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA · 2021 to 2025
$2.8M
Deep Learning for Characterizing Knee Joint Degeneration Predicting Progression of Osteoarthritis and Total Knee ReplacementR61AR073552 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA, PEDOIA, VALENTINA · 2018 to 2019
$871k
Multidimensional MRI-based Non-Euclidean Deep Learning to Study OsteoarthritisR00AR070902 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PEDOIA, VALENTINA · 2018 to 2020
$747k
Deep Learning for Characterizing Knee Joint Degeneration Predicting Progression of Osteoarthritis and Total Knee ReplacementR33AR073552 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MAJUMDAR, SHARMILA, PEDOIA, VALENTINA · 2020 to 2020
$404k
NIAMS NIH HHS P50 AR060752NIAMS NIH HHS R00 AR070902NIAMS NIH HHS R00AR070902NIAMS NIH HHS R01 AR046905NIAMS NIH HHS R01 AR069006NIAMS NIH HHS R01AR069006NIAMS NIH HHS R01 AR078762NIAMS NIH HHS R01 AR078917NIAMS NIH HHS R33 AR073552NIAMS NIH HHS R61 AR073552NIAMS NIH HHS UH3 AR076724
6 · The paper itself

Abstract

Precision medicine in musculoskeletal imaging requires scalable measurement infrastructure. We developed a modular system that converts routine MRI into standardized quantitative biomarkers suitable for clinical decision support. Promptable foundation segmenters (SAM, SAM2, MedSAM) were fine-tuned across heterogeneous musculoskeletal datasets and coupled to automated detection for fully automatic prompting. Fine-tuned segmentations yielded clinically reliable measurements with high concordance to expert annotations across cartilage, bone, and soft tissue biomarkers. Using the same measurements, we demonstrate two applications: (i) a three-stage knee triage cascade that reduces verification workload while maintaining sensitivity, and (ii) 48-month landmark models that forecast knee replacement and incident osteoarthritis with favorable calibration and net benefit across clinically relevant thresholds. Our model-agnostic, open-source architecture enables independent validation and development. This work validates a pathway from automated measurement to clinical decision: reliable biomarkers drive both workload optimization today and patient risk stratification tomorrow, and the developed framework shows how foundation models can be operationalized within precision medicine systems.

Identifiers

PMID41876760
PMCPMC13187405

What OpenQuestion holds

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Registered trials

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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.