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Related Concept Videos

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Deep Learning Estimation of 24-2 Visual Field Map From Optic Nerve Head Optical Coherence Tomography Angiography.

Golnoush Mahmoudinezhad1, Sasan Moghimi1, Liyang Ru2

  • 1Department of Ophthalmology, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family.

Journal of Glaucoma
|September 9, 2025
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Summary

Artificial intelligence accurately estimates visual field maps using OCTA images. This deep learning approach may reduce the need for frequent visual field testing.

Keywords:
24-2artificial intelligencedeep learningglaucomaoptical coherence tomography angiographyvisual field

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Visual field (VF) testing is crucial for diagnosing and monitoring glaucoma and other optic nerve head (ONH) conditions.
  • Optical coherence tomography angiography (OCTA) provides detailed microvascular information of the ONH.
  • Current VF testing can be time-consuming and may not capture subtle changes effectively.

Purpose of the Study:

  • To develop and evaluate deep learning (DL) models for estimating 24-2 visual field (VF) maps from OCTA optic nerve head (ONH) en face images.
  • To compare the performance of DL models against traditional linear regression (LR) methods.

Main Methods:

  • Trained DL models on 3148 VF OCTA image pairs from 994 participants, utilizing radial peripapillary capillary (RPC), superficial, and choroidal ONH vascular density (VD) layers.
  • Estimated 24-2 mean deviation (MD), pattern standard deviation (PSD), total deviation (TD), and pattern deviation (PD) values.
  • Assessed model accuracy using mean absolute error (MAE) and Pearson correlation coefficient (R).

Main Results:

  • DL models significantly outperformed LR models in estimating VF values across all tested ONH layers (P <0.001).
  • Using RPC, DL achieved an R of 0.79 and MAE of 1.77 dB for MD estimation.
  • DL models using combined ONH layers showed slight improvements for MD and TD estimations compared to individual layers.

Conclusions:

  • Deep learning models applied to OCTA images demonstrate high accuracy in estimating 24-2 visual field maps.
  • Leveraging ONH microvascular information from OCTA via DL holds potential for reducing the frequency of VF testing.
  • This AI-driven approach may offer a more efficient method for monitoring optic nerve health.