Evidence map›Paper›PMID 34780803›Full record

ArticleAmerican journal of ophthalmology2022

Deep Learning Image Analysis of Optical Coherence Tomography Angiography Measured Vessel Density Improves Classification of Healthy and Glaucoma Eyes.

Christopher Bowd, Akram Belghith, Linda M Zangwill, Mark Christopher, Michael H Goldbaum, Rui Fan, Jasmin Rezapour, Sasan Moghimi, Alireza Kamalipour, Huiyuan Hou and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

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

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Review
  6. Review
  7. Article
  8. Review
  9. Advances in OCT Angiography.Translational vision science & technology · 2025
    Review
  10. Article
  11. Observational
  12. Article
  13. Article
  14. Review
  15. A deep learning approach to investigate the filtration bleb functionality after glaucoma surgery: a preliminary study.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024
    Article
  16. Review
  17. Article
  18. Review
  19. Review
  20. 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

11 authors.

Christopher BowdFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA. Electronic address: cbowd@health.ucsd.edu.
Akram BelghithFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
Linda M ZangwillFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
Mark ChristopherFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
Michael H GoldbaumFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
Rui FanFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
Jasmin RezapourFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
Sasan MoghimiFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
Alireza KamalipourFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
Huiyuan HouFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
Robert N WeinrebFrom the Hamilton Glaucoma Center, Shiley Eye Institute, and The Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.

Funding

Vision BiostatisticsP30EY022589 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Derek Stuart Welsbie · 2012 to 2026
$10.3M
Diagnostic Innovations in Glaucoma Study (DIGS): High Myopia and Advanced DiseaseR01EY027510 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LINDA M ZANGWILL · 2017 to 2026
$5.3M
Ophthalmology and Visual Sciences Career Development K12 ProgramK12EY024225 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WEINREB, ROBERT N · 2015 to 2025
$4.3M
Diagnosis and Monitoring of Glaucoma with Optical Coherence Tomography AngiographyR01EY029058 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WEINREB, ROBERT N · 2018 to 2024
$4.2M
Machine Learning Methods for Detecting Disease-related Functional and Structural Change in GlaucomaR21EY027945 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BOWD, CHRISTOPHER · 2017 to 2018
$426k
AI-Based Identification of Rapid Glaucoma Progression to Guide Clinical Management and Accelerate Clinical TrialsR41EY034424 · NEI · AISIGHT HEALTH, INC. · PI TAFRESHI, ALI, ZANGWILL, LINDA M · 2022 to 2022
$240k
NEI NIH HHS K12 EY024225NEI NIH HHS P30 EY022589NEI NIH HHS R01 EY027510NEI NIH HHS R01 EY029058NEI NIH HHS R21 EY027945NEI NIH HHS R41 EY034424
6 · The paper itself

Abstract

purposeTo compare convolutional neural network (CNN) analysis of en face vessel density images to gradient boosting classifier (GBC) analysis of instrument-provided, feature-based optical coherence tomography angiography (OCTA) vessel density measurements and OCT retinal nerve fiber layer (RNFL) thickness measurements for classifying healthy and glaucomatous eyes.

designComparison of diagnostic approaches.

methodsA total of 130 eyes of 80 healthy individuals and 275 eyes of 185 glaucoma patients with optic nerve head (ONH) OCTA and OCT imaging were included. Classification performance of a VGG16 CNN trained and tested on entire en face 4.5 × 4.5-mm radial peripapillary capillary OCTA ONH images was compared to the performance of separate GBC models trained and tested on standard OCTA and OCT measurements. Five-fold cross-validation was used to test predictions for CNNs and GBCs. Areas under the precision recall curves (AUPRC) were calculated to control for training/test set size imbalance and were compared.

resultsAdjusted AUPRCs for GBC models were 0.89 (95% CI = 0.82, 0.92) for whole image vessel density GBC, 0.89 (0.83, 0.92) for whole image capillary density GBC, 0.91 (0.88, 0.93) for combined whole image vessel and whole image capillary density GBC, and 0.93 (0.91, 095) for RNFL thickness GBC. The adjusted AUPRC using CNN analysis of en face vessel density images was 0.97 (0.95, 0.99) resulting in significantly improved classification compared to GBC OCTA-based results and GBC OCT-based results (P ≤ 0.01 for all comparisons).

conclusionDeep learning en face image analysis improves on feature-based GBC models for classifying healthy and glaucoma eyes.

Indexed as

Deep LearningGlaucomaFluorescein AngiographyHumansIntraocular PressureRetinal Ganglion CellsRetinal VesselsTomography, Optical CoherenceVisual Fields

Identifiers

PMID34780803
PMCPMC10042115

What OpenQuestion holds

Textmetadata
LicenceTDM
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.