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Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device.

Marsida Bekollari1, Maria Dettoraki2, Valentina Stavrou2

  • 1Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece.

Diagnostics (Basel, Switzerland)
|February 24, 2024
PubMed
Summary

This study shows a new machine learning method for diagnosing glaucoma. The RETeval device achieved higher accuracy than optical coherence tomography (OCT) in classifying glaucoma patients.

Keywords:
RETevalglaucomamachine learning

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Area of Science:

  • Ophthalmology
  • Medical Technology
  • Artificial Intelligence

Background:

  • Glaucoma is a progressive optic nerve disease leading to vision loss and blindness.
  • Accurate and early diagnosis is crucial for managing glaucoma and preventing irreversible vision damage.

Purpose of the Study:

  • To investigate the efficacy of machine learning algorithms in classifying glaucoma patients.
  • To compare the diagnostic performance of the RETeval device versus conventional optical coherence tomography (OCT) for glaucoma detection.

Main Methods:

  • A machine learning approach was employed using Bayesian, Probabilistic Neural Network (PNN), and Support Vector Machines (SVM) classifiers.
  • Features were extracted from 172 eyes using both OCT and the RETeval portable device.
  • Glaucoma classification was analyzed based on eye selection (right/left) and gender.

Main Results:

  • The RETeval device demonstrated significantly higher classification accuracy compared to the OCT system across all metrics.
  • Accuracy improvements with RETeval were observed for overall participants (14.7%), eye selection (13.4% right, 29.3% left), and gender (25.6% male, 22.6% female).
  • Support Vector Machines (SVM) emerged as the most effective classifier among the three tested.

Conclusions:

  • The RETeval device offers a superior diagnostic advantage over OCT for machine learning-based glaucoma patient classification.
  • This finding supports the potential of advanced portable diagnostic tools integrated with AI for improved glaucoma management.