Evidence map›Paper›PMID 41132873›Full record

ArticleFrontiers in neurology2025

Evaluation of two AI techniques for the detection of new T2/FLAIR lesions in the follow-up of multiple sclerosis patients.

Milica Mastilović, Olivier Heinzlef, Christian Federau, Verónica Muñoz-Ramírez, Marie Blanchere, Jasmina Boban, Francois Cotton, Myriam Edjlali

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology, 2025. 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
0cells of the map it votes in
0citing 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

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

8 authors.

Milica MastilovićLaboratoire d'imagerie Biomédicale Multimodale (BioMaps), Université Paris-Saclay CEA, CNRS, Inserm, Service Hopsitalier Frédéric Joliot, Orsay, France.
Olivier HeinzlefDepartment of Neurology, Poissy-Saint-Germain-en-Laye Hospital, Poissy, France.
Christian FederauAI Medical AG, Zollikon, Switzerland.
Verónica Muñoz-RamírezPixyl Research and Development Laboratory, Grenoble, France.
Marie BlanchereDepartment of Neurology, Poissy-Saint-Germain-en-Laye Hospital, Poissy, France.
Jasmina BobanFaculty of Medicine, University of Novi Sad, Novi Sad, Serbia.
Francois CottonRadiology Department Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Oullins-Pierre-Bénite, France.
Myriam EdjlaliLaboratoire d'imagerie Biomédicale Multimodale (BioMaps), Université Paris-Saclay CEA, CNRS, Inserm, Service Hopsitalier Frédéric Joliot, Orsay, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Multiple sclerosis is an inflammatory demyelinating disease of the CNS. Annual MRI exams are crucial for disease monitoring. Interpreting high T2/FLAIR lesion loads can be laborious. AI aids in lesion detection, and choosing between different solutions can be challenging. Aim: This study compares two distinct software, Pixyl.Neuro.MS Methods: Retrospective analysis included follow-up MRIs from 35 MS patients. Pixyl.Neuro.MS Results: Pixyl.Neuro.MS Conclusion: Both AI software have been found to enhance NL detection in MS patients, outperforming standard methods. These tools offer crucial advantages for accurate disease monitoring.

Indexed as

artificial intelligencedeep learninglesion evaluationmagnetic resonance imagingmultiple sclerosis

Identifiers

PMID41132873
PMCPMC12540099

What Socratic holds

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LicenceCC BY
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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 Socratic graph.