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A Clinically Explainable AI-Based Grading System for Age-Related Macular Degeneration Using Optical Coherence

M Elsharkawy, A Sharafeldeen, F Khalifa

IEEE Journal of Biomedical and Health Informatics

|January 17, 2024

Summary

We developed an explainable AI system for diagnosing age-related macular degeneration (AMD) using optical coherence tomography (OCT) images. The system accurately differentiates between various AMD stages and other retinal conditions.

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Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

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Published on: May 26, 2023

Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Age-related macular degeneration (AMD) is a leading cause of vision loss.
Accurate and timely diagnosis of AMD and its subtypes is crucial for effective treatment.
Current diagnostic methods can be subjective and time-consuming.

Purpose of the Study:

To develop an automated, explainable AI (xAI) system for diagnosing AMD from OCT images.
To differentiate between normal retinas, various AMD grades, and non-AMD diseases.
To extract and utilize clinically meaningful imaging markers for diagnosis.

Main Methods:

Utilized optical coherence tomography (OCT) B-scan images.
Developed an xAI system integrating DeepLabV3+ and a novel CNN model.
Extracted key clinical imaging markers: subretinal/intraretinal fluid, choroidal hypertransmission, merged retinal layers, drusen, and retinal layer thickness.
Employed a hierarchical decision tree for classification.

Main Results:

The xAI system achieved 90.82% accuracy in a multi-way classification task on 1285 OCT images.
Successfully differentiated normal retinas, early, intermediate, geographic atrophy (GA), and wet AMD, as well as non-AMD diseases.
Demonstrated the system's capability to mimic physician perception in diagnosing AMD.

Conclusions:

The proposed xAI system offers a promising automated approach for AMD diagnosis.
The system can accurately classify various stages of AMD and distinguish from other retinal pathologies.
Explainable AI holds potential for improving diagnostic efficiency and accuracy in ophthalmology.