<a><b>Objective:</b></a> Diabetic
macular edema (DME) is the primary cause of vision loss among individuals with
diabetes mellitus (DM). We developed, validated, and tested a deep-learning
(DL) system for classifying DME using images from three common commercially available
optical coherence tomography (OCT) devices.
<p><b>Research
Design and Methods:</b> We trained and validated two versions of
a multi-task convolution neural network (CNN) to classify DME (center-involved
DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D)
volume-scans and two-dimensional (2D) B-scans respectively. For both 3D and 2D
CNNs, we employed the residual network (ResNet) as the backbone. For the 3D
CNN, we used a 3D version of ResNet-34 with the last fully connected layer
removed as the feature extraction module. A total of 73,746 OCT images were
used for training and primary validation. External testing was performed using 26,981
images across seven independent datasets from Singapore, Hong Kong, the US,
China, and Australia. </p>
<p><b>Results:</b> In
classifying the presence or absence of DME, the DL system achieved area under
the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920–0.954),
0.958 (0.930–0.977),
and 0.965 (0.948–0.977) for
primary dataset obtained from Cirrus, Spectralis, and Triton OCTs respectively,
in addition to AUROCs greater than 0.906 for the external datasets. For the
further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were
0.968 (0.940–0.995),
0.951 (0.898–0.982),
and 0.975 (0.947–0.991) for
the primary dataset and greater than 0.894 for the external datasets. </p>
<p><b>Conclusion:</b> We
demonstrated excellent performance with a DL system for the automated
classification of DME, highlighting its potential as a promising second-line screening
tool
for patients with DM, which may
potentially create a more effective triaging mechanism to eye clinics. </p>
Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images
