Evidence map›Paper›PMID 41652557›Full record

ArticleBMC medical imaging2026

UMUD: a web application for easy access to musculoskeletal ultrasonography datasets.

Paul Ritsche, Fabio Sarto, Francesco Santini, Christoph Leitner, Martino V Franchi, Oliver Faude, Taija Finni, Olivier Seynnes, Neil Cronin

Abstract read
In one paragraph

Article in BMC medical imaging, 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

9 authors.

Paul Ritsche *Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland. paul.ritsche@unibas.ch.ORCID 0000-0001-9446-7872
Fabio Sarto *Department of Biomedical Sciences, University of Padova, Padova, Italy. fabio.sarto@unipd.it.ORCID 0000-0001-8572-5147
Francesco SantiniBasel Muscle MRI, Department of Biomedical Engineering, University of Basel, Basel, Switzerland.
Christoph LeitnerDepartment of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland. christoph.leitner@iis.ee.ethz.ch.
Martino V FranchiDepartment of Biomedical Sciences, University of Padova, Padova, Italy.
Oliver FaudeDepartment of Sport, Exercise and Health, University of Basel, Basel, Switzerland.
Taija FinniFaculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland.
Olivier SeynnesDepartment for Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway.
Neil CroninFaculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUltrasonography is widely used to assess skeletal muscle and tendon properties, such as architecture, cross-sectional area, and tissue stiffness. Despite its growing application in different scenarios, the lack of accessible and standardized public datasets limits large-scale studies and the development of image analysis algorithms. To address this, we developed the Universal Musculoskeletal Ultrasonography Database (UMUD), a platform designed to facilitate access to these datasets and foster standardization in musculoskeletal ultrasonography imaging research. UMUD is an online repository that aggregates and indexes metadata from publicly available musculoskeletal ultrasonography datasets hosted on platforms like the Open Science Framework and Zenodo. By offering detailed metadata descriptors—such as muscle group, ultrasound device, participant demographics, and publication details—UMUD streamlines dataset discovery and exploration through search and visualization tools.

resultsSo far, UMUD includes 14 Datasets, including 76.124 images (and/or 2.674 videos) derived from 1.901 participants. The platform also includes benchmark datasets for training and validating image analysis algorithms. These comprise multi-expert analyses of muscle architecture and panoramic cross-sectional area images, a dataset containing overlays of muscle geometry for teaching muscle architecture analysis, and labeled datasets for training deep learning models. Additionally, UMUD lists state-of-the-art automated analysis algorithms to support users in their application.

conclusionUMUD addresses relevant challenges in musculoskeletal ultrasonography by providing a centralized, standardized repository of datasets and tools. Thus, it promotes transparency and innovation in the field, supporting reproducible research and advancements in automated image analysis. Future developments include adding more datasets, refining user functionalities, and introducing community-driven challenges to enhance its impact.

Indexed as

Databases, FactualMuscle, SkeletalMusculoskeletal SystemAlgorithmsHumansInternetUltrasonographyBenchmarksDatabaseData sharingMuscle architectureMuscle cross-sectional areaOpen accessOpen sciencePublic repositoriesTendonUltrasound imaging

Identifiers

PMID41652557
PMCPMC12998083

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.