Evidence map›Paper›PMID 42904093›Full record

ArticleOsteoarthritis and cartilage open2026

OpenMSK: A modular open-source framework for comprehensive analysis of imaging biomarkers in knee osteoarthritis.

Ananya Goyal, Francesca Belibi, Vyoma Sahani, Rune Pedersen, Yael Vainberg, Ashley Williams, Constance Chu, Bryan Haddock, Garry E Gold, Akshay S Chaudhari and 2 more

Abstract read
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In one paragraph

Article in Osteoarthritis and cartilage open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Ananya GoyalDepartment of Radiology, Stanford University, CA, USA.
Francesca BelibiDepartment of Radiology, Stanford University, CA, USA.
Vyoma SahaniDepartment of Radiology, Stanford University, CA, USA.
Rune PedersenDepartment of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, Copenhagen University Hospital, Denmark.
Yael VainbergDepartment of Radiology, Stanford University, CA, USA.
Ashley WilliamsDepartment of Orthopaedic Surgery, Stanford University, CA, USA.
Constance ChuDepartment of Orthopaedic Surgery, Stanford University, CA, USA.
Bryan HaddockDepartment of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, Copenhagen University Hospital, Denmark.
Garry E GoldDepartment of Radiology, Stanford University, CA, USA.
Akshay S ChaudhariDepartment of Radiology, Stanford University, CA, USA.
Feliks KoganDepartment of Radiology, Stanford University, CA, USA.
Anthony A GattiDepartment of Radiology, Stanford University, CA, USA.

Funding

Imaging of Joint Response to Physiological Stress with Age, Sex and in OsteoarthritisR01AR079431 · NIAMS · STANFORD UNIVERSITY · PI Feliks Kogan · 2022 to 2026
$2.9M
NIAMS NIH HHS R01 AR079431
6 · The paper itself

Abstract

Objective: To develop and validate a fully automated, extendable, open-source AI-driven pipeline capable of knee magnetic resonance imaging (MRI) segmentation and quantitative extraction of MRI and positron emission tomography (PET) imaging biomarkers. Design: For model training, open-source knee MRI data from 347 knees acquired across 7 scanners using multiple gradient-echo sequences were used. An independent test set of 20 knees from 3T PET-MRI examinations were used for evaluation in this prospective reliability and agreement study. The pipeline segmented femoral, tibial, and patellar bones and cartilage, and medial and lateral menisci using three different deep learning models. Quantitative outputs included cartilage thickness and T Results: Automated segmentations demonstrated high spatial agreement with manual annotations, with DSCs ranging from 0.84 to 0.98 across all tissues. Quantitative PET-MRI biomarkers generally demonstrated high agreement, although confidence intervals were wide for several biomarkers. Conclusion: OpenMSK enables multi-tissue knee segmentation and generation of quantitative MRI and PET biomarkers, with performance assessed against two independent annotators and across different U-Net-based segmentation architectures. As a freely available, modular platform, OpenMSK lowers technical barriers to whole-joint quantitative imaging and provides a standardized foundation for OA research and clinical translation.

Indexed as

Automated image segmentation and analysisKnee MRIMusculoskeletal imagingOpen-source MSK imaging pipelinePET-MR image analysis

Identifiers

PMID42904093
PMCPMC13682006

What OpenQuestion holds

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Registered trials

None linked

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.