Evidence map›Paper›PMID 38354971›Full record

ArticleAmerican journal of ophthalmology2024

Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning.

Vahid Mohammadzadeh, Sean Wu, Sajad Besharati, Tyler Davis, Arvind Vepa, Esteban Morales, Kiumars Edalati, Mahshad Rafiee, Arthur Martinyan, David Zhang and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Artificial Intelligence in Glaucoma Detection and Progression.Advances in ophthalmology and optometry · 2026
    Article
  2. Review
  3. Article
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  12. Review
  13. Big data for imaging assessment in glaucoma.Taiwan journal of ophthalmology
    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

13 authors.

Vahid MohammadzadehFrom the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.
Sean WuDepartment of Computer Science, Pepperdine University (S.W., F.S.), Malibu, California, USA.
Sajad BesharatiFrom the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.
Tyler DavisDepartment of Computer Science, University of California Los Angeles (T.D., A.V., F.S.), Los Angeles, California, USA.
Arvind VepaDepartment of Computer Science, University of California Los Angeles (T.D., A.V., F.S.), Los Angeles, California, USA.
Esteban MoralesFrom the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.
Kiumars EdalatiFrom the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.
Mahshad RafieeFrom the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.
Arthur MartinyanFrom the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.
David ZhangFrom the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.
Fabien ScalzoDepartment of Computer Science, Pepperdine University (S.W., F.S.), Malibu, California, USA; Department of Computer Science, University of California Los Angeles (T.D., A.V., F.S.), Los Angeles, California, USA.
Joseph CaprioliFrom the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.
Kouros Nouri-MahdaviFrom the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA. Electronic address: nouri-mahdavi@jsei.ucla.edu.

Funding

Detection of Disease Progression in Advanced GlaucomaR01EY029792 · NEI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI NOURI-MAHDAVI, KOUROS · 2020 to 2024
$1.9M
NEI NIH HHS R01 EY029792
6 · The paper itself

Abstract

purposeIdentifying glaucoma patients at high risk of progression based on widely available structural data is an unmet task in clinical practice. We test the hypothesis that baseline or serial structural measures can predict visual field (VF) progression with deep learning (DL).

designDevelopment of a DL algorithm to predict VF progression.

methods3,079 eyes (1,765 patients) with various types of glaucoma and ≥5 VFs, and ≥3 years of follow-up from a tertiary academic center were included. Serial VF mean deviation (MD) rates of change were estimated with linear-regression. VF progression was defined as negative MD slope with p<0.05. A Siamese Neural Network with ResNet-152 backbone pre-trained on ImageNet was designed to predict VF progression using serial optic-disc photographs (ODP), and baseline retinal nerve fiber layer (RNFL) thickness. We tested the model on a separate dataset (427 eyes) with RNFL data from different OCT. The Main Outcome Measure was Area under ROC curve (AUC).

resultsBaseline average (SD) MD was 3.4 (4.9)dB. VF progression was detected in 900 eyes (29%). AUC (95% CI) for model incorporating baseline ODP and RNFL thickness was 0.813 (0.757-0.869). After adding the second and third ODPs, AUC increased to 0.860 and 0.894, respectively (p<0.027). This model also had highest AUC (0.911) for predicting fast progression (MD rate <1.0 dB/year). Model's performance was similar when applied to second dataset using RNFL data from another OCT device (AUC=0.893; 0.837-0.948).

conclusionsDL model predicted VF progression with clinically relevant accuracy using baseline RNFL thickness and serial ODPs and can be implemented as a clinical tool after further validation.

Indexed as

Deep LearningDisease ProgressionIntraocular PressureNerve FibersOptic DiskRetinal Ganglion CellsROC CurveTomography, Optical CoherenceVisual FieldsVisual Field TestsAgedAlgorithmsArea Under CurveFemaleFollow-Up StudiesGlaucoma

Identifiers

PMID38354971
PMCPMC11226195

What OpenQuestion holds

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LicenceCC BY-NC
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.