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. 2022 Nov 9;3(1):100245.
doi: 10.1016/j.xops.2022.100245. eCollection 2023 Mar.

Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT

Pengxiao Zang  1   2 , Tristan T Hormel  1 , Thomas S Hwang  1 , Steven T Bailey  1 , David Huang  1 , Yali Jia  1   2
Affiliations

Deep-Learning-Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT

Pengxiao Zang et al. Ophthalmol Sci. .

Abstract

Purpose: Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies.

Design: Cross sectional study.

Participants: Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma.

Methods: The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis.

Main outcome measures: The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework.

Results: For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02.

Conclusions: Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.

Financial disclosures: Proprietary or commercial disclosure may be found after the references.

Keywords: 3D, 3-dimensional; AMD, age-related macular degeneration; AUC, area under the curve; Age-related macular degeneration; CAM, class activation map; DR, diabetic retinopathy; Deep learning; Diabetic retinopathy; Glaucoma; OCT; OCTA, OCT angiography; ROC, receiver operating characteristic.

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Figures

Figure 1
Figure 1
Automated diagnostic framework for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma using combined volumetric OCT and OCT angiography (OCTA) data as inputs. Structural OCT and OCTA data volumes are resampled and combined to form the input for a semi-sequential classifier. The first part of the classifier then diagnoses DR and AMD. Data not diagnosed as DR and AMD by the first part are fed to the second part for glaucoma. Eyes not diagnosed with DR, AMD, or glaucoma can be considered normal or as having other diseases. For the diagnosis of any disease, the network also generates 3-dimensional (3D) class activation maps (CAMS).
Figure 2
Figure 2
Receiver operating characteristic (ROC) and precision-recall curves derived from fivefold cross-validation for the diagnosis of diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma based on the full framework. The area under the curve (AUC) was calculated for both the curves. The models achieved AUCs of 0.95 ± 0.01 and 0.91 for ROC and precision recall, respectively, for the diagnosis of DR, AUCs of 0.98 ± 0.01 and 0.95 for ROC and precision recall, respectively, for the diagnosis of AMD, and AUCs of 0.91 ± 0.02 and 0.71 for ROC and precision recall, respectively, for the diagnosis of glaucoma. In addition, the precision-recall curve for glaucoma looked different from that of the other 2 diseases because glaucoma prediction was a combination of the 2 parts of the semisequential classifier. std. dev. = standard deviation.
Figure 3
Figure 3
Confusion matrices for the first part of the semisequential classifier (left) and the full semisequential classifier (right) based on the overall results of fivefold cross-validation. AMD = age-related macular degeneration; DR = diabetic retinopathy.
Figure 4
Figure 4
Class activation map based on the diabetic retinopathy output layer of the semisequential classifier for an eye with correctly classified diabetic retinopathy. A, OCT angiography (OCTA) en face projection of the superficial vascular complex ([SVC] inner 80% of the ganglion cell complex). The nonperfusion and low-perfusion areas were highlighted by the class activation map. B, Corresponding B scan at the position of the red line in (A). C, Structural OCT en face image of the ellipsoid zone ([EZ] the boundary between the outer nuclear layer and the EZ to the boundary between the EZ and the retinal pigment epithelium). D, Corresponding B scan at the location of the red line in (C).
Figure 5
Figure 5
Class activation map based on the age-related macular degeneration output layer of the semisequential classifier for an eye with correctly classified age-related macular degeneration. A, OCT angiography (OCTA) en face projection of the superficial vascular complex ([SVC] inner 80% of the ganglion cell complex). B, Corresponding B scan at the position of the red line in (A). C, Structural OCT en face image of the ellipsoid zone ([EZ] the boundary between the outer nuclear layer and the EZ to the boundary between the EZ and the retinal pigment epithelium). D, Corresponding B scan at the location of the red line in (C). The drusen area was highlighted by the class activation map.
Figure 6
Figure 6
Class activation map based on the glaucoma output layer of the semisequential classifier for an eye with correctly classified glaucoma. A, OCT angiography (OCTA) en face projection of the superficial vascular complex ([SVC] inner 80% of the ganglion cell complex). The low-perfusion area is highlighted in (B). Corresponding B scan at the position of the red line in (A). C, Structural OCT en face image of the inner retina (the boundary between the vitreous and the inner limiting membrane to the boundary between the outer plexiform layer and the outer nuclear layer). D, Corresponding B scan at the location of the red line in (C). The region of the vanished nerve fiber layer was highlighted.
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