Multimodal 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

doi:10.1155/2020/7493419

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

J Ophthalmol. 2020 Jan 13:2020:7493419. doi: 10.1155/2020/7493419. eCollection 2020.

Authors

Ehsan Vaghefi^{1

2}, Sophie Hill³, Hannah M Kersten¹, David Squirrell³

Affiliations

¹ School of Optometry and Vision Sciences, University of Auckland, Auckland, New Zealand.
² Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
³ Department of Ophthalmology, University of Auckland, Auckland, New Zealand.

Abstract

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.