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Updated: Dec 15, 2025

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A deep learning approach to predict visual field using optical coherence tomography.

Keunheung Park1,2, Jinmi Kim3, Jiwoong Lee1,2

  • 1Department of Ophthalmology, Pusan National University College of Medicine, Busan, South Korea.

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|July 7, 2020
PubMed
Summary
This summary is machine-generated.

A novel deep learning model accurately predicts visual fields using combined optical coherence tomography (OCT) scans. This AI approach aids clinicians in assessing visual field loss, especially for patients unable to complete traditional exams.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Visual field testing is crucial for diagnosing and monitoring conditions like glaucoma.
  • Traditional visual field tests can be challenging for some patients.
  • Optical coherence tomography (OCT) provides detailed retinal imaging.

Purpose of the Study:

  • To develop and evaluate a deep learning model for predicting visual fields from OCT images.
  • To assess the model's performance in normal and glaucoma patient groups.

Main Methods:

  • A deep learning architecture based on Inception V3 was employed.
  • Combined macular ganglion cell-inner plexiform layer (mGCIPL) and peripapillary retinal nerve fibre layer (pRNFL) OCT data were used.
  • A convolutional neural network predicted visual fields, with performance measured by root mean square error (RMSE).

Main Results:

  • The overall RMSE was 4.79 ± 2.56 dB.
  • Glaucoma patients showed a higher RMSE (5.27 dB) compared to normal subjects (3.27 dB).
  • The model effectively predicted visual fields, demonstrating potential clinical utility.

Conclusions:

  • Deep learning effectively predicts visual fields using combined OCT imaging.
  • This AI-driven method offers a promising alternative for visual field assessment, particularly for non-cooperative patients.