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

Abstract EN

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.