Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study

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)...

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Bibliographic Details
Published in:Journal of ophthalmology 2020, Vol.2020 (2020), p.1-7
Main Authors: Squirrell, David, Kersten, Hannah M., Hill, Sophie, Vaghefi, Ehsan
Format: Article
Language:English
Subjects:
Accuracy
Age
Automation
Cable television broadcasting industry
Deep learning
Diabetic retinopathy
Diagnostic imaging
Feasibility studies
Glaucoma
International economic relations
Macular degeneration
Neural networks
Patients
Physiological aspects
Software
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Summary: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.
ISSN:2090-004X
2090-0058
DOI:10.1155/2020/7493419