Evidence map›Paper›PMID 34328544›Full record

ArticleJournal of neurology2022

Application of deep-learning to the seronegative side of the NMO spectrum.

Laura Cacciaguerra, Loredana Storelli, Marta Radaelli, Sarlota Mesaros, Lucia Moiola, Jelena Drulovic, Massimo Filippi, Maria A Rocca

Abstract read
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In one paragraph

Article in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 3 pooled it
–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

10 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Double-negative neuromyelitis optica spectrum disorder.Multiple sclerosis (Houndmills, Basingstoke, England) · 2023
    Review
  10. 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

8 authors.

Laura CacciaguerraNeuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Loredana StorelliNeuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Marta RadaelliNeurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Sarlota MesarosClinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
Lucia MoiolaNeurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Jelena DrulovicClinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia.
Massimo FilippiNeuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Maria A RoccaNeuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy. rocca.mara@hsr.it.ORCID http://orcid.org/0000-0003-2358-4320

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo apply a deep-learning algorithm to brain MRIs of seronegative patients with neuromyelitis optica spectrum disorders (NMOSD) and NMOSD-like manifestations and assess whether their structural features are similar to aquaporin-4-seropositive NMOSD or multiple sclerosis (MS) patients. PATIENTS AND

methodsWe analyzed 228 T2- and T1-weighted brain MRIs acquired from aquaporin-4-seropositive NMOSD (n = 85), MS (n = 95), aquaporin-4-seronegative NMOSD [n = 11, three with anti-myelin oligodendrocyte glycoprotein antibodies (MOG)], and aquaporin-4-seronegative patients with NMOSD-like manifestations (idiopathic recurrent optic neuritis and myelitis, n = 37), who were recruited from February 2010 to December 2019. Seventy-three percent of aquaporin-4-seronegative patients with NMOSD-like manifestations also had a clinical follow-up (median duration of 4 years). The deep-learning neural network architecture was based on four 3D convolutional layers. It was trained and validated on MRI scans of aquaporin-4-seropositive NMOSD and MS patients and was then applied to aquaporin-4-seronegative NMOSD and NMOSD-like manifestations. Assignment of unclassified aquaporin-4-seronegative patients was compared with their clinical follow-up.

resultsThe final algorithm differentiated aquaporin-4-seropositive NMOSD and MS patients with an accuracy of 0.95. All aquaporin-4-seronegative NMOSD and 36/37 aquaporin-4-seronegative patients with NMOSD-like manifestations were classified as NMOSD. Anti-MOG patients had a similar probability of being NMOSD or MS. At clinical follow-up, one unclassified aquaporin-4-seronegative patient evolved to MS, three developed NMOSD, and the others did not change phenotype.

conclusionsOur findings support the inclusion of aquaporin4-seronegative patients into NMOSD and suggest a possible expansion to aquaporin-4-seronegative unclassified patients with NMOSD-like manifestations. Anti-MOG patients are likely to have intermediate brain features between NMOSD and MS.

Indexed as

Deep LearningNeuromyelitis OpticaAquaporin 4AutoantibodiesHumansMyelin-Oligodendrocyte GlycoproteinAquaporin 4AutoantibodiesMyelin-Oligodendrocyte GlycoproteinArtificial intelligenceIdiopathicMRIMultiple sclerosisNeuromyelitis optica spectrum disordersSeronegative

Identifiers

PMID34328544

What OpenQuestion holds

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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.