Evidence map›Paper›PMID 41035621›Full record

ArticleImaging neuroscience (Cambridge, Mass.)2025

Deep-learning segmentation of the substantia nigra from multiparametric MRI: Application to Parkinson's disease.

Peder A G Lillebostad, Tormund H Njølstad, Signe Hogstad, Frank Riemer, Simon U Kverneng, Kjersti E Stige, Martin Biermann, Mandar Jog, Sagar Buch, E Mark Haacke and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Imaging neuroscience (Cambridge, Mass.), 2025. 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

12 authors.

Peder A G LillebostadDepartment of Biomedicine, University of Bergen, Bergen, Norway.
Tormund H NjølstadDepartment of Radiology, Haukeland University Hospital, Bergen, Norway.ORCID https://orcid.org/0000-0003-4024-3819
Signe HogstadMohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen, Norway.
Frank RiemerMohn Medical Imaging and Visualization Center, Department of Radiology, Haukeland University Hospital, Bergen, Norway.ORCID https://orcid.org/0000-0002-3805-5221
Simon U KvernengNeuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway.ORCID https://orcid.org/0000-0002-8507-8127
Kjersti E StigeNeuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway.
Martin BiermannDepartment of Radiology, Haukeland University Hospital, Bergen, Norway.ORCID https://orcid.org/0000-0002-5550-1035
Mandar JogNeuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway.
Sagar BuchDepartment of Neurology, Wayne State University, Detroit, MI, United States.ORCID https://orcid.org/0000-0001-9483-269X
E Mark HaackeDepartment of Neurology, Wayne State University, Detroit, MI, United States.
Charalampos TzoulisNeuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway.ORCID https://orcid.org/0000-0003-0341-5191
Arvid LundervoldDepartment of Biomedicine, University of Bergen, Bergen, Norway.ORCID https://orcid.org/0000-0002-0032-4182

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Loss of dopaminergic neurons in the substantia nigra (SN) pars compacta (SNc) is a pathological hallmark of Parkinson's disease (PD). This is accompanied by a reduction of the dopamine synthesis byproduct neuromelanin (NM), which can be detected

Indexed as

domain shiftmultiparametric MRIneuromelanin MRIParkinson’s diseasesegmentationsubstantia nigra

Identifiers

PMID41035621
PMCPMC12479381

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.