Evidence map›Paper›PMID 34954220›Full record

ArticleOphthalmology. Glaucoma

A Case for the Use of Artificial Intelligence in Glaucoma Assessment.

Joel S Schuman, Maria De Los Angeles Ramos Cadena, Rebecca McGee, Lama A Al-Aswad, Felipe A Medeiros, Collaborative Community on Ophthalmic Imaging Executive Committee and Glaucoma Workgroup

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ophthalmology. Glaucoma. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Artificial Intelligence for Optical Coherence Tomography in Glaucoma.Translational vision science & technology · 2025
    Review
  7. Article
  8. Review
  9. Review
  10. 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

6 authors.

Joel S SchumanDepartment of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Departments of Biomedical Engineering and Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Center for Neural Science, NYU, New York, New York; Neuroscience Institute, NYU Langone Health, New York, New York. Electronic address: joel.schuman@nyu.edu.
Maria De Los Angeles Ramos CadenaDepartment of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York.
Rebecca McGeeDepartment of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York.
Lama A Al-AswadDepartment of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Population Health, NYU Langone Health, NYU Grossman School of Medicine, New York, New York.
Felipe A MedeirosDepartment of Ophthalmology, Duke University School of Medicine, Durham, North Carolina; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina.
Collaborative Community on Ophthalmic Imaging Executive Committee and Glaucoma Workgroup

Funding

Novel Glaucoma Diagnostics for Structure and Function - Renewal - 1R01EY013178 · NEI · WILLS EYE HEALTH SYSTEM · PI Joel S Schuman · 2000 to 2026
$17.5M
NEI NIH HHS R01 EY013178
6 · The paper itself

Abstract

We hypothesize that artificial intelligence (AI) applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community on Ophthalmic Imaging meeting, "The Future of Artificial Intelligence-Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways," held virtually September 3-4, 2020. The Collaborative Community on Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology. It was 1 of the first 2 such organizations officially designated by the Food and Drug Administration in September 2019 in response to their announcement of the collaborative community program as a strategic priority for 2018-2020. Further information on the CCOI can be found online at their website (https://www.cc-oi.org/about). Artificial intelligence for glaucoma diagnosis would have high utility globally, because access to care is limited in many parts of the world and half of all people with glaucoma are unaware of their illness. The application of AI technology to glaucoma diagnosis has the potential to broadly increase access to care worldwide, in essence flattening the Earth by providing expert-level evaluation to individuals even in the most remote regions of the planet.

Indexed as

Artificial IntelligenceGlaucomaDiagnostic ImagingHumansArtificial intelligenceDeep learningGlaucomaNeural networksOCT

Identifiers

PMID34954220
PMCPMC9133028

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.