Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study. (13th January 2020)

• Record Type: Journal Article
• Authors: Vaghefi, Ehsan; Hill, Sophie; Kersten, Hannah M.; Squirrell, David
• Other Names: Kusuhara Sentaro Academic Editor.
• Is Part Of: Journal of ophthalmology. Volume 2020(2020)
• Publication Date: 2020-01-13
• Subjects: Ophthalmology -- Periodicals; Eye Diseases; Ophthalmology; Ophthalmology; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 617.7
• DOI: 10.1155/2020/7493419
• Languages: English
• ISSNs: 2090-004X

Abstract

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.