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A deep learning model incorporating spatial and temporal information successfully detects visual field worsening

Jasdeep Sabharwal1, Kaihua Hou2, Patrick Herbert2

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A deep learning model (DLM) accurately detects glaucoma worsening using visual field (VF) tests. This AI tool shows promise in assisting clinicians with consistent and reproducible patient monitoring for irreversible blindness.

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Glaucoma is a primary cause of irreversible blindness globally.
  • Visual field (VF) testing is crucial for monitoring glaucoma progression.
  • Current methods for identifying VF worsening can lack consistency and reproducibility.

Purpose of the Study:

  • To develop and evaluate a deep learning model (DLM) for detecting visual field (VF) worsening in glaucoma patients.
  • To compare the DLM's performance against clinician assessments.
  • To establish a reliable method for identifying glaucoma progression.

Main Methods:

  • A DLM was trained and tested on a dataset of 5099 glaucoma patients (8705 eyes) with VF testing data.
  • A consensus of six algorithmic methods was used as the reference standard for VF worsening.
  • Clinician assessments were evaluated against this reference standard.

Main Results:

  • The DLM achieved a high Area Under the Curve (AUC) of 0.94 for identifying VF worsening.
  • Even with limited recent data, the DLM maintained strong performance (AUC 0.78).
  • Clinician assessment showed a lower AUC of 0.64, indicating less accuracy than the DLM.

Conclusions:

  • The developed DLM demonstrates high accuracy and reproducibility in identifying glaucoma-related VF worsening.
  • DLMs can serve as valuable tools to support clinicians in routine glaucoma care.
  • AI-powered analysis of VF tests offers a promising approach to mitigate vision loss from glaucoma.