Evidence map›Paper›PMID 38143166›Full record

ArticleZeitschrift fur medizinische Physik2024

Predicting disease-related MRI patterns of multiple sclerosis through GAN-based image editing.

Daniel Güllmar, Wei-Chan Hsu, Jürgen R Reichenbach

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Zeitschrift fur medizinische Physik, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.8field-weighted citation impact, top 12% of its field
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

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. Artificial intelligence in medical physics.Zeitschrift fur medizinische Physik · 2024
    Article
  3. Review
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

3 authors at 1 institution in 1 country.

Daniel GüllmarMedical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena 07743, Germany; Michael Stifel Center for Data-Driven and Simulation Science, Jena 07743, Germany. Electronic address: daniel.guellmar@med.uni-jena.de.
Wei-Chan HsuMedical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena 07743, Germany; Michael Stifel Center for Data-Driven and Simulation Science, Jena 07743, Germany.
Jürgen R ReichenbachMedical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena 07743, Germany; Michael Stifel Center for Data-Driven and Simulation Science, Jena 07743, Germany.
Jena University Hospital · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMultiple sclerosis (MS) is a complex neurodegenerative disorder that affects the brain and spinal cord. In this study, we applied a deep learning-based approach using the StyleGAN model to explore patterns related to MS and predict disease progression in magnetic resonance images (MRI).

methodsWe trained the StyleGAN model unsupervised using T

resultsOur results show that MS progression can be simulated by manipulating MR images in the latent space, as evidenced by brain volume loss on both T

conclusionOverall, this study demonstrates the potential of the StyleGAN model in medical imaging to study image markers and to shed more light on the relationship between brain atrophy and MS progression through corresponding manipulations in the latent space.

Indexed as

Disease ProgressionMagnetic Resonance ImagingMultiple SclerosisAdultBrainDeep LearningFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedBrain atrophyGenerative adversarial network (GAN)Latent spaceMagnetic resonance imaging (MRI)Multiple sclerosis (MS)

Identifiers

PMID38143166
PMCPMC11156773
OpenAlexW4390146178

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.