Evidence map›Paper›PMID 39117359›Full record

Observational studyThe British journal of ophthalmology2024

Detection of glaucoma progression on longitudinal series of en-face macular optical coherence tomography angiography images with a deep learning model.

Vahid Mohammadzadeh, Youwei Liang, Sasan Moghimi, Pengtao Xie, Takashi Nishida, Golnoush Mahmoudinezhad, Medi Eslani, Evan Walker, Alireza Kamalipour, Eleonora Micheletti and 5 more

Registry-linked trialAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in The British journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00221897 (Diagnostic Innovations in Glaucoma Study), which is not on this map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

NCT00221897 recruitingnot on this map

Diagnostic Innovations in Glaucoma Study

TypeobservationalSponsorUniversity of California, San DiegoRan1995 to 2030Enrolled3,000ConditionsPrimary Open Angle Glaucoma, Myopia
3 · Its place in the literature

Who cites it

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

  1. Analysing myopia in Europe: A comprehensive meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Pooled it
  2. Review
  3. Article
  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

15 authors.

Vahid Mohammadzadeh *Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.
Youwei Liang *Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA.
Sasan MoghimiViterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.ORCID 0000-0002-9375-4711
Pengtao XieDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA.
Takashi NishidaViterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.ORCID 0000-0002-8312-6623
Golnoush MahmoudinezhadViterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.
Medi EslaniViterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.
Evan WalkerViterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.ORCID 0000-0001-8566-0371
Alireza KamalipourViterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.
Eleonora MichelettiDepartment of Surgical & Clinical, Diagnostic and Pediatric Sciences, Section of Ophthalmology, University of Pavia, Pavia, Lombardia, Italy.
Jo-Hsuan WuViterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.ORCID 0000-0002-9435-746X
Mark ChristopherViterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.
Linda M ZangwillViterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA.ORCID 0000-0002-1143-5224
Tara JavidiDepartment of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, USA.
Robert N WeinrebViterbi Family Department of Ophthalmology, University of California San Diego, La Jolla, California, USA rweinreb@ucsd.edu.ORCID 0000-0001-9553-3202

Funding

Vision BiostatisticsP30EY022589 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Derek Stuart Welsbie · 2012 to 2026
$10.3M
Diagnostic Innovations in Glaucoma:Structural AssessmentR01EY011008 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZANGWILL, LINDA M · 1995 to 2015
$8.7M
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
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
African Descent and Glaucoma Evaluation (ADAGES) II: Glaucoma ProgressionR01EY019869 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZANGWILL, LINDA M · 2010 to 2014
$2.8M
African Descent and Glaucoma Evaluation (ADAGES) IV: Alterations of the lamina cribrosa in progressionR01EY026574 · NEI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI FAZIO, MASSIMO ANTONIO, GIRKIN, CHRISTOPHER ANTHONY · 2017 to 2020
$2.6M
Monitoring of Glaucoma Patients in Advanced DiseaseR01EY034148 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Sasan Moghimi Araghi · 2022 to 2026
$2.5M
Multimodal Artificial Intelligence to Predict Glaucomatous Progression and Surgical InterventionR01EY034146 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BAXTER, SALLY LIU, CHRISTOPHER, MARK · 2022 to 2025
$1.8M
Deep Learning Approaches to Detect Glaucoma and Predict Progression from Spectral Domain Optical Coherence TomographyR00EY030942 · NEI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHRISTOPHER, MARK · 2023 to 2025
$747k
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 P30 EY022589NEI NIH HHS R00 EY030942NEI NIH HHS R01 EY011008NEI NIH HHS R01 EY019869NEI NIH HHS R01 EY026574NEI NIH HHS R01 EY027510NEI NIH HHS R01 EY029058NEI NIH HHS R01 EY034146NEI NIH HHS R01 EY034148NEI NIH HHS R41 EY034424
6 · The paper itself

Abstract

BACKGROUND/

aimsTo design a deep learning (DL) model for the detection of glaucoma progression with a longitudinal series of macular optical coherence tomography angiography (OCTA) images.

methods202 eyes of 134 patients with open-angle glaucoma with ≥4 OCTA visits were followed for an average of 3.5 years. Glaucoma progression was defined as having a statistically significant negative 24-2 visual field (VF) mean deviation (MD) rate. The baseline and final macular OCTA images were aligned according to centre of fovea avascular zone automatically, by checking the highest value of correlation between the two images. A customised convolutional neural network (CNN) was designed for classification. A comparison of the CNN to logistic regression model for whole image vessel density (wiVD) loss on detection of glaucoma progression was performed. The performance of the model was defined based on the confusion matrix of the validation dataset and the area under receiver operating characteristics (AUC).

resultsThe average (95% CI) baseline VF MD was -3.4 (-4.1 to -2.7) dB. 28 (14%) eyes demonstrated glaucoma progression. The AUC (95% CI) of the DL model for the detection of glaucoma progression was 0.81 (0.59 to 0.93). The sensitivity, specificity and accuracy (95% CI) of DL model were 67% (34% to 78%), 83% (42% to 97%) and 80% (52% to 95%), respectively. The AUC (95% CI) for the detection of glaucoma progression based on the logistic regression model was lower than the DL model (0.69 (0.50 to 0.88)).

conclusionThe optimised DL model detected glaucoma progression based on longitudinal macular OCTA images showed good performance. With external validation, it could enhance detection of glaucoma progression. TRIAL REGISTRATION NUMBER: NCT00221897.

Indexed as

Deep LearningDisease ProgressionGlaucoma, Open-AngleIntraocular PressureTomography, Optical CoherenceVisual FieldsAgedFemaleFluorescein AngiographyFollow-Up StudiesHumansMacula LuteaMaleMiddle AgedOptic DiskRetinal Ganglion CellsGlaucoma

Identifiers

PMID39117359
PMCPMC11585450

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Registered trials

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.