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.
Highlights
In the developed world, age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in the population over 60 years old [1, 2]
Optical coherence tomography (OCT) and fundus autofluorescence (FAF) have joined the battery of imaging techniques that are considered essential for the monitoring of non-neovascular AMD. is list has more recently been joined by optical coherence tomography angiography (OCT-A) and multicolour confocal scanning laser ophthalmoscopy (SLO) [7]
We wanted to compare the accuracy of different convolutional neural network (CNN) designs, trained on the same dataset, to investigate whether combing imaging modalities improved the ability of the CNN to accurately identify the 3 distinct clinical groups under test
Summary
Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in the population over 60 years old [1, 2]. Characteristic features of non-neovascular AMD include macular drusen, RPE abnormalities, and, in the late stage, geographic atrophy [3]. Pigment abnormalities detected by colour fundus photography (CFP) are well recognised to be one of the major risk factors for the development of late stage AMD [3,4,5,6]. Optical coherence tomography (OCT) and fundus autofluorescence (FAF) have joined the battery of imaging techniques that are considered essential for the monitoring of non-neovascular AMD. Within the field of ophthalmology, automated image analysis has been applied to the detection of diabetic. Concentrating on macular disease, both semiautomated and automated techniques have already been used and validated in the detection of drusen, reticular drusen, and geographic atrophy [15,16,17,18,19]. E purpose of this study is to determine whether a multimodal deep learning approach; training the CNN on OCT, OCT-A, and CFP, will diagnose intermediate dry AMD more accurately, when compared to conventional CNN trained on the single modalities of CFP, OCT, and OCT-A
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