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Automated Identification of Incomplete and Complete Retinal Epithelial Pigment and Outer Retinal Atrophy Using

Jeffrey N Chiang¹, Giulia Corradetti², Muneeswar Gupta Nittala³

¹Department of Computational Medicine, University of California Los Angeles, Los Angeles, California.

Ophthalmology. Retina

|August 22, 2022

Summary

A deep learning algorithm accurately detects incomplete and complete retinal pigment epithelial and outer retinal atrophy (iRORA and cRORA) in OCT scans. This tool shows potential as a diagnostic screening method, reducing manual annotation time for age-related macular degeneration.

Keywords:

Age-related macular degeneration Deep learning Geographic atrophy Incomplete retinal pigment epithelial and outer retinal atrophy Nonneovascular macular degeneration

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Published on: January 22, 2018

Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Age-related macular degeneration (AMD) is a leading cause of vision loss.
Accurate detection of retinal pigment epithelial and outer retinal atrophy (iRORA and cRORA) is crucial for AMD management.
Manual identification of these lesions in OCT scans is time-consuming.

Purpose of the Study:

To develop and validate a deep learning algorithm for automated detection of iRORA and cRORA.
To assess the algorithm's performance on independent datasets.
To evaluate the potential of the algorithm as a diagnostic screening tool.

Main Methods:

A Resnet18 deep learning model was trained on OCT B-scan volumes annotated for iRORA and cRORA.
The model was evaluated on two independent testing datasets.
Performance was measured using AUROC and AUPRC, compared to human graders.

Main Results:

The algorithm demonstrated high performance in detecting iRORA and cRORA on independent datasets.
AUROC scores for iRORA and cRORA ranged from 0.61-0.84, and AUPRC scores ranged from 0.61-0.83.
The model achieved sensitivity comparable to human graders.

Conclusions:

A deep learning model can accurately identify iRORA and cRORA lesions in OCT volumes.
The algorithm offers a potential solution to reduce manual annotation burden.
The developed tool could serve as an effective diagnostic screening method for AMD.