Multi-Modal retinal image analysis via deep learning for the diagnosis of intermediate dry age-related macular degeneration: a feasibility study

Ehsan Vaghefi*, Sophie Hill, Hannah M. Kersten, David Squirrell

*Corresponding author for this work

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Abstract

Background and Objective. To determine if using a multi-input deep learning approach in the image analysis of optical coherence tomography (OCT), OCT angiography (OCT-A), and colour fundus photographs increases the accuracy of a CNN to diagnose intermediate dry age-related macular degeneration (AMD). Patients and Methods. Seventy-five participants were recruited and divided into three cohorts: young healthy (YH), old healthy (OH), and patients with intermediate dry AMD. Colour fundus photography, OCT, and OCT-A scans were performed. The convolutional neural network (CNN) was trained on multiple image modalities at the same time. Results. The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. Conclusions. Here we demonstrate that superior diagnostic accuracy can be achieved when deep learning is combined with multimodal image analysis.
Original languageEnglish
Article number7493419
Number of pages7
JournalJournal of Ophthalmology
Volume2020
DOIs
Publication statusPublished - 13 Jan 2020

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