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Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
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Automatic retinal image analysis methods using colour fundus images for screening glaucomatous optic neuropathy.

Chuying Shi1,2, Jack Lee1, Di Shi3

  • 1Center for Clinical Research and Biostatistics, Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China.

BMJ Open Ophthalmology
|September 10, 2024
PubMed
Summary

An automatic retinal image analysis (ARIA) method effectively screens for glaucomatous optic neuropathy (GON) using optical coherence tomography (OCT) data. The combined feature model demonstrated superior accuracy for glaucoma detection in retinal images.

Keywords:
Diagnostic tests/InvestigationGlaucoma

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucomatous optic neuropathy (GON) is a leading cause of irreversible blindness worldwide.
  • Early detection and screening are crucial for preventing vision loss.
  • Non-mydriatic retinal images offer a scalable approach for glaucoma screening.

Purpose of the Study:

  • To train an automatic retinal image analysis (ARIA) method for screening GON.
  • To utilize optical coherence tomography (OCT) results for enhanced labeling and validation.
  • To evaluate different models for GON classification based on retinal image features.

Main Methods:

  • Utilized 1338 non-mydriatic retinal images with paired OCT scans for training and 10-fold cross-validation.
  • Employed OCT results as the reference standard for image labeling.
  • Developed and compared classification models using features from entire images, optic disc ROI, macula ROI, and combined features.

Main Results:

  • The ARIA method achieved high performance metrics across different feature sets.
  • Models using optic disc and macula regions showed promising results.
  • The model combining all features yielded the highest sensitivity (92.5%), specificity (92.1%), and accuracy (92.4%), significantly outperforming others (p<0.001).

Conclusions:

  • A novel ARIA method effectively detects glaucoma using color fundus retinal images.
  • The combined feature model represents a significant advancement in automated glaucoma screening.
  • This approach offers a potentially cost-effective and accurate alternative to conventional screening methods.