Evidence map›Paper›PMID 37833037›Full record

ArticleThe British journal of ophthalmology2024

Prediction of visual field progression with serial optic disc photographs using deep learning.

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article 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. 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.

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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 MohammadzadehDepartment of Ophthalmology, Jules Stein Eye Institute, UCLA, Los Angeles, California, USA.
Sean WuDepartment of Computer Science, Pepperdine University, Malibu, California, USA.
Tyler DavisDepartment of Computer Science, University of California Los Angeles, Los Angeles, California, USA.
Arvind VepaDepartment of Computer Science, University of California Los Angeles, Los Angeles, California, USA.
Esteban MoralesDepartment of Ophthalmology, Jules Stein Eye Institute, UCLA, Los Angeles, California, USA.
Sajad BesharatiDepartment of Ophthalmology, Jules Stein Eye Institute, UCLA, Los Angeles, California, USA.
Kiumars EdalatiDepartment of Ophthalmology, Jules Stein Eye Institute, UCLA, Los Angeles, California, USA.
Jack MartinyanDepartment of Ophthalmology, Jules Stein Eye Institute, UCLA, Los Angeles, California, USA.
Mahshad RafieeDepartment of Ophthalmology, Jules Stein Eye Institute, UCLA, Los Angeles, California, USA.
Arthur MartynianDepartment of Ophthalmology, Jules Stein Eye Institute, UCLA, Los Angeles, California, USA.
Fabien ScalzoDepartment of Computer Science, University of California Los Angeles, Los Angeles, California, USA.
Joseph CaprioliDepartment of Ophthalmology, Jules Stein Eye Institute, UCLA, Los Angeles, California, USA.ORCID 0000-0002-2383-7263
Kouros Nouri-MahdaviDepartment of Ophthalmology, Jules Stein Eye Institute, UCLA, Los Angeles, California, USA nouri-mahdavi@jsei.ucla.edu.ORCID 0000-0001-9403-8904

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

aimWe tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up.

methods3919 eyes (2259 patients) with ≥2 ODPs at least 2 years apart, and ≥5 24-2 VF exams spanning ≥3 years of follow-up were included. Serial VF mean deviation (MD) rates of change were estimated starting at the fifth visit and subsequently by adding visits until final visit. VF progression was defined as a statistically significant negative slope at two consecutive visits and final visit. We built a twin-neural network with ResNet50-backbone. A pair of ODPs acquired up to a year before the VF progression date or the last VF in non-progressing eyes were included as input. Primary outcome measures were area under the receiver operating characteristic curve (AUC) and model accuracy.

resultsThe average (SD) follow-up time and baseline VF MD were 8.1 (4.8) years and -3.3 (4.9) dB, respectively. VF progression was identified in 761 eyes (19%). The median (IQR) time to progression in progressing eyes was 7.3 (4.5-11.1) years. The AUC and accuracy for predicting VF progression were 0.862 (0.812-0.913) and 80.0% (73.9%-84.6%). When only fast-progressing eyes were considered (MD rate < -1.0 dB/year), AUC increased to 0.926 (0.857-0.994).

conclusionsA deep learning model can predict subsequent glaucoma progression from longitudinal ODPs with clinically relevant accuracy. This model may be implemented, after validation, for predicting glaucoma progression in the clinical setting.

Indexed as

Deep LearningDisease ProgressionOptic DiskPhotographyVisual FieldsVisual Field TestsAgedFemaleFollow-Up StudiesGlaucomaHumansIntraocular PressureMaleMiddle AgedOptic Nerve DiseasesRetrospective StudiesField of visionGlaucomaImagingOptic Nerve

Identifiers

PMID37833037
PMCPMC11014894

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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.