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Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study.
- Source :
-
Journal of Ophthalmology . 1/13/2020, p1-8. 8p. - Publication Year :
- 2020
-
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]
Details
- Language :
- English
- ISSN :
- 2090004X
- Database :
- Academic Search Index
- Journal :
- Journal of Ophthalmology
- Publication Type :
- Academic Journal
- Accession number :
- 141194554
- Full Text :
- https://doi.org/10.1155/2020/7493419