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Related Experiment Video

Updated: Dec 23, 2025

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A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells).

Eduardo M Normando1,2, Tim E Yap1,2, John Maddison3

  • 1ICORG, Imperial College London , London, UK.

Expert Review of Molecular Diagnostics
|April 21, 2020
PubMed
Summary
This summary is machine-generated.

An automated AI method accurately detects DARC spots to predict glaucoma progression. This AI-aided biomarker offers objective measurement for early detection and drug testing in glaucoma patients.

Keywords:
Artificial IntelligenceBiomarkerCNNapoptosisglaucomaimaging

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Biomarker Development

Background:

  • Glaucoma poses a risk of rapid progression and blindness.
  • Detection-of-Apoptosing-Retinal-Cells (DARC) is a novel method for visualizing apoptotic retinal cells.
  • Accurate prediction of glaucoma progression is crucial for timely intervention.

Purpose of the Study:

  • To develop an automated Convolutional Neural Network (CNN)-aided method for DARC spot detection.
  • To utilize DARC spot detection for predicting glaucoma progression.
  • To establish an objective biomarker for glaucoma management.

Main Methods:

  • DARC images were obtained from healthy controls (n=40) and glaucoma patients (n=20).
  • A CNN-aided algorithm was trained and validated using manual DARC spot counts from control subjects.
  • The algorithm was tested on glaucoma eyes, comparing its performance to OCT-retinal nerve fiber layer measurements.

Main Results:

  • The algorithm achieved 97.0% accuracy, 91.1% sensitivity, and 97.1% specificity in spot detection compared to manual grading in controls.
  • In glaucoma patients, the algorithm showed 85.7% sensitivity, 91.7% specificity, and an AUC of 0.89.
  • A significantly higher DARC count (p=0.0044) was observed in patients who later progressed.

Conclusions:

  • The CNN-enabled algorithm provides an automated and objective measure of DARC.
  • This AI-aided approach can serve as a biomarker for predicting glaucoma progression.
  • The method facilitates the testing of new glaucoma drugs.