1. Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study.
- Author
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Vaghefi, Ehsan, Hill, Sophie, Kersten, Hannah M., and Squirrell, David
- Subjects
AGING ,ANGIOGRAPHY ,CLINICAL trials ,COMBINED modality therapy ,DIGITAL image processing ,PHOTOGRAPHY ,RETINA ,RETINAL degeneration ,PILOT projects ,OPTICAL coherence tomography ,DEEP learning - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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