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Predicting Products: SN1 vs. SN202:27

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Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration
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Predicting HFA 30-2 Visual Fields with Deep Learning from Multimodal OCT-Fundus Feature Fusion and Structure-Function

İlknur Tuncer Fırat1, Murat Fırat2, Haci Erbali1

  • 1Faculty of Medicine, Inonu University, Ophthalmology, Malatya, Türkiye.

Journal of Imaging Informatics in Medicine
|January 20, 2026
PubMed
Summary
This summary is machine-generated.

This study shows that artificial intelligence can predict visual field test results from eye scans, improving glaucoma diagnosis. Predictions are more accurate when structural and functional measures align, highlighting the importance of structure-function concordance.

Keywords:
Humphrey 30–2Optical Coherence TomographyStructure–function DiscordanceVision TransformerVisual Field

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis

Background:

  • Glaucoma is a primary cause of irreversible vision loss.
  • Visual field (VF) tests assess functional loss, while optical coherence tomography (OCT) and fundus imaging provide structural data.
  • VF testing can be subjective and exhibit variability, sometimes showing structure-function discordance (SFD).

Purpose of the Study:

  • To estimate Humphrey 30-2 visual field measures (mean deviation (MD), pattern standard deviation (PSD), and point-wise threshold sensitivity (TS)) in glaucoma/ocular hypertension (OHT) patients.
  • To utilize a Vision Transformer (ViT-B/32)-based feature-fusion approach combining OCT and fundus images.
  • To analyze the impact of SFD on prediction accuracy.

Main Methods:

  • Extracted visual features from optic disc photographs, fundus images, and retinal nerve fiber layer (RNFL) maps using ViT-B/32 models.
  • Developed a multimodal AI model integrating visual features with demographic and clinical data.
  • Employed probabilistic regression for global VF indices (MD, PSD) and a location-aware network for point-wise TS prediction.

Main Results:

  • The AI model achieved mean absolute errors (MAE) of 2.26 dB for MD, 1.42 dB for PSD, and 2.96 dB for mean TS.
  • Excluding eyes with SFD improved MAEs to 1.82 dB (MD), 1.30 dB (PSD), and 2.12 dB (mean TS), with increased proportions within ±2 dB.
  • Model performance was notably better in clinically concordant cases.

Conclusions:

  • ViT-B/32-based deep feature fusion accurately predicts VF metrics from multimodal structural images.
  • Structure-function discordance negatively impacts prediction reliability.
  • AI predictions are more dependable in cases with OCT-VF concordance, and SFD should be considered when interpreting results.