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Related Experiment Video

Updated: Jul 16, 2025

Evaluation of Capillary and Other Vessel Contribution to Macular Perfusion Density Measured with Optical Coherence Tomography Angiography
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Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography

Golnoush Mahmoudinezhad1, Sasan Moghimi1, Jiacheng Cheng2

  • 1From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California.

American Journal of Ophthalmology
|September 21, 2023
PubMed
Summary

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Deep learning models accurately estimate visual field loss from OCTA scans, outperforming linear regression. This technology can improve clinical decisions and patient care for those at risk of central vision damage.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Visual field (VF) loss is a key indicator of glaucoma and other optic neuropathies.
  • Optical coherence tomography angiography (OCTA) provides detailed vessel density (VD) maps of the retina.
  • Current methods for assessing VF may be invasive or time-consuming.

Purpose of the Study:

  • To develop and validate deep learning (DL) models for estimating central visual field (VF) parameters using OCTA vessel density (VD) measurements.
  • To compare the performance of DL models against traditional linear regression (LR) models in VF estimation.

Main Methods:

  • A dataset of 1051 OCTA and VF pairs from healthy and glaucoma patients was utilized.
  • DL models were trained on en face macula VD images from OCTA to predict mean deviation (MD), pattern standard deviation (PSD), total deviation (TD), and pattern deviation (PD) values.

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  • Model accuracy was assessed using mean absolute error (MAE) and R-squared (R²) values, comparing DL predictions to actual VF measurements.

    • Main Results:

    • DL models achieved high accuracy in predicting 10-2 mean deviation (MD), with an R² of 0.85 and MAE of 1.76 dB.
    • The DL model significantly outperformed linear regression (LR) for estimating pointwise total deviation (TD) values, achieving an average MAE of 2.48 dB and R² of 0.69.
    • DL models demonstrated superior performance across all tested sectors compared to LR models.

    Conclusions:

    • Deep learning models can accurately estimate visual field loss directly from OCTA images.
    • The application of DL to OCTA may enhance clinical decision-making for glaucoma and other optic neuropathies.
    • This approach holds potential for improving individualized patient care and risk stratification for central VF damage.