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

M. Elsharkawy, A. Sharafeldeen, F. Khalifa, A. Soliman, A. Elnakib, M. Ghazal, A. Sewelam, A. Thanos, H. S. Sandhu, A. El-Baz

Research output: Contribution to journalArticlepeer-review

Abstract

We propose an automated, explainable artificial intelligence (xAI) system for age-related macular degeneration (AMD) diagnosis. Mimicking the physician&#x0027;s perceptions, the proposed xAI system is capable of deriving clinically meaningful features from optical coherence tomography (OCT) B-scan images to differentiate between a normal retina, different grades of AMD (early, intermediate, geographic atrophy (GA), inactive wet or active neovascular disease [exudative or wet AMD]), and non-AMD diseases. Particularly, we extract retinal OCT-based clinical imaging markers that are correlated with the progression of AMD, which include: (<italic>i</italic>) subretinal tissue, sub-retinal pigment epithelial tissue, intraretinal fluid, subretinal fluid, and choroidal hypertransmission detection using a DeepLabV3+ network; (<italic>ii</italic>) detection of merged retina layers using a novel convolutional neural network model; (<italic>iii</italic>) drusen detection based on 2D curvature analysis; (<italic>iv</italic>) estimation of retinal layers&#x0027; thickness, and first-order and higher-order reflectivity features. Those clinical features are used to grade a retinal OCT in a hierarchical decision tree process. The first step looks for severe disruption of retinal layers&#x0027; indicative of advanced AMD. These cases are analyzed further to diagnose GA, inactive wet AMD, active wet AMD, and non-AMD diseases. Less severe cases are analyzed using a different pipeline to identify OCT with AMD-specific pathology, which is graded as intermediate-stage or early-stage AMD. The remainder is classified as either being a normal retina or having other non-AMD pathology. The proposed system in the multi-way classification task, evaluated on 1285 OCT images, achieved 90.82&#x0025; accuracy. These promising results demonstrated the capability to automatically distinguish between normal eyes and all AMD grades in addition to non-AMD diseases.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
DOIs
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management

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