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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Glaucoma is a leading cause of irreversible blindness.
  • Accurate prediction of visual field progression is crucial for timely intervention.
  • Current methods for forecasting visual field changes have limitations.

Purpose of the Study:

  • To evaluate the efficacy of deep learning networks in predicting future Humphrey Visual Fields (HVFs).
  • To develop an artificial neural network capable of forecasting visual field progression.

Main Methods:

  • Extracted over 1.7 million data points from 32,443 24-2 HVFs (1998-2018).
  • Employed ten-fold cross-validation and a held-out test set for model development.
  • Trained a deep learning artificial neural network (CascadeNet-5) using transfer learning.

Main Results:

  • The best model, CascadeNet-5, achieved a point-wise mean absolute error (PMAE) of 2.47 dB.
  • Deep learning significantly outperformed linear models in prediction accuracy.
  • Models predicted future HVFs up to 5.5 years with a 0.92 correlation in Mean Deviation (MD).

Conclusions:

  • Deep learning networks effectively learn spatio-temporal visual field changes.
  • AI models can generate accurate future HVF predictions using single, real-world datasets.
  • This technology offers a promising tool for monitoring glaucoma progression.