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Deep Learning-Based Glaucoma Screening Using Regional RNFL Thickness in Fundus Photography.

Hyunmo Yang¹, Yujin Ahn^1,2, Sanzhar Askaruly¹

¹Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea.

Diagnostics (Basel, Switzerland)

|November 26, 2022

Summary

A new deep learning model predicts retinal nerve fiber layer (RNFL) thickness from fundus photos, enabling early glaucoma screening. This approach offers high sensitivity and specificity, making glaucoma detection more accessible.

Keywords:

color fundus photographs convolutional neural networks glaucoma normal-tension glaucoma optical coherence tomography retinal nerve fiber layer screening

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma is a progressive optic neuropathy requiring early detection to prevent vision loss.
Optical coherence tomography (OCT) accurately measures retinal nerve fiber layer (RNFL) thickness but is costly and less accessible.
Fundus photography is widely used but requires expert interpretation for early glaucoma detection.

Purpose of the Study:

To develop and validate a deep learning model for predicting RNFL thickness from fundus photography.
To assess the efficacy of this model as a screening tool for early glaucoma detection.

Main Methods:

A convolutional neural network (CNN) was trained using fundus images and corresponding OCT-measured RNFL thickness data.
The model estimated RNFL thickness in 12 optic disc regions from fundus photos of normal tension glaucoma (NTG) patients.

Main Results:

The deep learning model achieved 92% sensitivity and 86.9% specificity for glaucoma screening.
At 80% specificity, regional RNFL thickness analysis yielded 90.7% sensitivity, outperforming global RNFL thickness analysis (71.2% sensitivity).

Conclusions:

Deep learning-based prediction of regional RNFL thickness from fundus images shows promise for accessible early glaucoma screening.
This technique can identify localized optic nerve head damage, improving glaucoma detection rates.