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Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between

Jonghoon Shin1,2, Sungjoon Kim3, Jinmi Kim4

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

Translational Vision Science & Technology
|June 4, 2021
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Summary
This summary is machine-generated.

A deep learning model accurately estimates visual fields (VF) from optical coherence tomography (OCT) images. Swept-source OCT (SS-OCT) showed superior performance compared to spectral-domain OCT (SD-OCT) in this estimation.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Visual field (VF) testing is crucial for diagnosing and monitoring glaucoma.
  • Current VF testing methods can be time-consuming and challenging for patients.
  • Optical coherence tomography (OCT) provides detailed retinal imaging that may correlate with VF defects.

Purpose of the Study:

  • To develop and compare deep learning models for estimating visual fields (VF) from spectral-domain OCT (SD-OCT) and swept-source OCT (SS-OCT) images.
  • To evaluate the performance of these models across different glaucoma severities and retinal regions.

Main Methods:

  • Two Inception-ResNet-v2 deep learning models were trained to estimate 24-2 VF from SS-OCT and SD-OCT images.
  • Performance was assessed using root mean square error (RMSE) between actual and estimated VFs.
  • Comparisons were made based on glaucoma severity, Garway-Heath sectorization, and central/peripheral regions.

Main Results:

  • The deep learning model using SS-OCT achieved a significantly lower global estimation error (4.51 ± 2.54 dB) compared to SD-OCT (5.29 ± 2.68 dB) (P < 0.001).
  • SS-OCT demonstrated superior performance in most sectors, except the inferonasal sector in normal/early glaucoma.
  • While central region errors increased in advanced glaucoma for both OCT types, SS-OCT remained significantly better in peripheral regions.

Conclusions:

  • Deep learning models can effectively estimate visual fields from OCT images.
  • SS-OCT-based models provide more accurate VF estimations than SD-OCT models.
  • This technology holds potential for aiding clinical VF assessment and can be integrated into OCT devices.