Evidence map›Paper›PMID 37868793›Full record

ArticleOphthalmology science

An Artificial Intelligence Enabled System for Retinal Nerve Fiber Layer Thickness Damage Severity Staging.

Siamak Yousefi, Xiaoqin Huang, Asma Poursoroush, Julek Majoor, Hans Lemij, Koen Vermeer, Tobias Elze, Mengyu Wang, Kouros Nouri-Mahdavi, Vahid Mohammadzadeh and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
1.4field-weighted citation impact, top 19% of its field
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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. 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 at 6 institutions in 3 countries.

Siamak YousefiDepartment of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee.
Xiaoqin HuangDepartment of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee.
Asma PoursoroushDepartment of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee.
Julek MajoorRotterdam Ophthalmic Institute, The Rotterdam Eye Hospital, Rotterdam, The Netherlands.
Hans LemijRotterdam Ophthalmic Institute, The Rotterdam Eye Hospital, Rotterdam, The Netherlands.
Koen VermeerRotterdam Ophthalmic Institute, The Rotterdam Eye Hospital, Rotterdam, The Netherlands.
Tobias ElzeSchepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachussetts.
Mengyu WangSchepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachussetts.
Kouros Nouri-MahdaviDepartment of Ophthalmology, University of California Los Angeles, Los Angeles, California.
Vahid MohammadzadehDepartment of Ophthalmology, University of California Los Angeles, Los Angeles, California.
Paolo BrusiniDepartment of Ophthalmology, "Città di Udine" Health Center, Udine, Italy.
Chris JohnsonDepartment of Ophthalmology & Visual Sciences, University of Iowa Hospitals and Clinics, Iowa City, Iowa.
Rotterdam Eye Hospital · NLUniversity of Tennessee Health Science Center · USMassachusetts Eye and Ear Infirmary · USUniversity of California, Los Angeles · USUniversity of Iowa Hospitals and Clinics · USUniversity of Udine · IT

Funding

VISION RESEARCHP30EY003790 · NEI · SCHEPENS EYE RESEARCH INSTITUTE · PI Patricia Ann D'Amore · 1985 to 2026
$25.5M
Personalizing Glaucoma Diagnosis by Disease Specific Patterns and Individual Eye AnatomyR01EY030575 · NEI · SCHEPENS EYE RESEARCH INSTITUTE · PI ELZE, TOBIAS · 2019 to 2023
$2.4M
Predicting the risk of glaucoma from structural, functional, and genetic factors using artificial intelligenceR01EY033005 · NEI · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI YOUSEFI, SIAMAK · 2022 to 2025
$2.4M
Personalizing Circumpapillary Retinal Nerve Fiber Layer Thickness Norms for GlaucomaR21EY035298 · NEI · SCHEPENS EYE RESEARCH INSTITUTE · PI WANG, MENGYU · 2023 to 2023
$557k
Improved Glaucoma Monitoring Using Artificial-Intelligence Enabled DashboardR21EY031725 · NEI · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI YOUSEFI, SIAMAK · 2020 to 2022
$545k
NEI NIH HHS P30 EY003790NEI NIH HHS R01 EY030575NEI NIH HHS R01 EY033005NEI NIH HHS R21 EY031725NEI NIH HHS R21 EY035298
6 · The paper itself

Abstract

Purpose: To develop an objective glaucoma damage severity classification system based on OCT-derived retinal nerve fiber layer (RNFL) thickness measurements. Design: Algorithm development for RNFL damage severity classification based on multicenter OCT data. Subjects and Participants: A total of 6561 circumpapillary RNFL profiles from 2269 eyes of 1171 subjects to develop models, and 2505 RNFL profiles from 1099 eyes of 900 subjects to validate models. Methods: We developed an unsupervised Main Outcome Measures: Accuracy, area under the receiver operating characteristic curve, and confusion matrix. Results: The Conclusions: Unsupervised machine learning discovered that the optimal RNFL thresholds for separating normal eyes and eyes with early, moderate, and advanced RNFL loss were 95 Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

Artificial intelligenceGlaucomaGlaucoma severity damageOptical coherence tomographyRetinal nerve fiber layer (RNFL)StagingUnsupervised machine learning

Identifiers

PMID37868793
PMCPMC10585627
OpenAlexW4386087298

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.