Evidence map›Paper›PMID 37917086›Full record

ArticleTranslational vision science & technology2023

Prediction of Central Visual Field Measures From Macular OCT Volume Scans With Deep Learning.

Vahid Mohammadzadeh, Arvind Vepa, Chuanlong Li, Sean Wu, Leila Chew, Golnoush Mahmoudinezhad, Evan Maltz, Serhat Sahin, Apoorva Mylavarapu, Kiumars Edalati and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Translational vision science & technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. 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

15 authors.

Vahid MohammadzadehGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Arvind VepaDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
Chuanlong LiDepartment of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Sean WuDepartment of Computer Science, Pepperdine University, Malibu, CA, USA.
Leila ChewGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Golnoush MahmoudinezhadGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Evan MaltzDepartment of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA.
Serhat SahinDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
Apoorva MylavarapuGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Kiumars EdalatiGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Jack MartinyanGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Dariush YalzadehGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Fabien ScalzoDepartment of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
Joseph CaprioliGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Kouros Nouri-MahdaviGlaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.

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

Purpose: Predict central 10° global and local visual field (VF) measurements from macular optical coherence tomography (OCT) volume scans with deep learning (DL). Methods: This study included 1121 OCT volume scans and 10-2 VFs from 289 eyes (257 patients). Macular scans were used to estimate 10-2 VF mean deviation (MD), threshold sensitivities (TS), and total deviation (TD) values at 68 locations. A three-dimensional (3D) convolutional neural network based on the 3D DenseNet121 architecture was used for prediction. We compared DL predictions to those from baseline linear models. We carried out 10-fold stratified cross-validation to optimize generalizability. The performance of the DL and baseline models was compared based on correlations between ground truth and predicted VF measures and mean absolute error (MAE; ground truth - predicted values). Results: Average (SD) MD was -9.3 (7.7) dB. Average (SD) correlations between predicted and ground truth MD and MD MAE were 0.74 (0.09) and 3.5 (0.4) dB, respectively. Estimation accuracy deteriorated with worsening MD. Average (SD) Pearson correlations between predicted and ground truth TS and MAEs for DL and baseline model were 0.71 (0.05) and 0.52 (0.05) (P < 0.001) and 6.5 (0.6) and 7.5 (0.5) dB (P < 0.001), respectively. For TD, correlation (SD) and MAE (SD) for DL and baseline models were 0.69 (0.02) and 0.48 (0.05) (P < 0.001) and 6.1 (0.5) and 7.8 (0.5) dB (P < 0.001), respectively. Conclusions: Macular OCT volume scans can be used to predict global central VF parameters with clinically relevant accuracy. Translational Relevance: Macular OCT imaging may be used to confirm and supplement central VF findings using deep learning.

Indexed as

Deep LearningTomography, Optical CoherenceEyeHumansNeural Networks, ComputerVisual Fields

Identifiers

PMID37917086
PMCPMC10627306

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.