Evidence map›Paper›PMID 34945039›Full record

ArticleJournal of clinical medicine2021

Deep Learning to Distinguish

Alexandra Miere, Olivia Zambrowski, Arthur Kessler, Carl-Joe Mehanna, Carlotta Pallone, Daniel Seknazi, Paul Denys, Francesca Amoroso, Eric Petit, Eric H Souied

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Artificial intelligence applications in inherited retinal dystrophies.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  7. Article
  8. Review
  9. Article
  10. Artificial intelligence in retinal disease: clinical application, challenges, and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023
    Review
  11. 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

10 authors.

Alexandra MiereDepartment of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, University Paris-Est Créteil, 94000 Créteil, France.ORCID 0000-0003-4123-8210
Olivia ZambrowskiDepartment of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, University Paris-Est Créteil, 94000 Créteil, France.
Arthur KesslerEPISEN-ISBS, University Paris-Est Créteil, 94000 Créteil, France.
Carl-Joe MehannaDepartment of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, University Paris-Est Créteil, 94000 Créteil, France.ORCID 0000-0002-6758-7972
Carlotta PalloneDepartment of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, University Paris-Est Créteil, 94000 Créteil, France.
Daniel SeknaziDepartment of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, University Paris-Est Créteil, 94000 Créteil, France.
Paul DenysDepartment of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, University Paris-Est Créteil, 94000 Créteil, France.
Francesca AmorosoDepartment of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, University Paris-Est Créteil, 94000 Créteil, France.
Eric PetitLaboratory of Images, Signals and Intelligent Systems (LISSI, EA N° 3956), University Paris-Est Créteil, 94400 Vitry-sur-Seine, France.
Eric H SouiedDepartment of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, University Paris-Est Créteil, 94000 Créteil, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

(1) Background: Recessive Stargardt disease (STGD1) and multifocal pattern dystrophy simulating Stargardt disease ("pseudo-Stargardt pattern dystrophy", PSPD) share phenotypic similitudes, leading to a difficult clinical diagnosis. Our aim was to assess whether a deep learning classifier pretrained on fundus autofluorescence (FAF) images can assist in distinguishing

Indexed as

deep learningfundus autofluorescenceinherited retinal diseasesretinal imaging

Identifiers

PMID34945039
PMCPMC8708395

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.