Dataset Information

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography.

ABSTRACT:

Purpose

Reliable classification of referable and vision threatening diabetic retinopathy (DR) is essential for patients with diabetes to prevent blindness. Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages over fundus photographs. We evaluated a deep-learning-aided DR classification framework using volumetric OCT and OCTA.

Methods

Four hundred fifty-six OCT and OCTA volumes were scanned from eyes of 50 healthy participants and 305 patients with diabetes. Retina specialists labeled the eyes as non-referable (nrDR), referable (rDR), or vision threatening DR (vtDR). Each eye underwent a 3 × 3-mm scan using a commercial 70 kHz spectral-domain OCT system. We developed a DR classification framework and trained it using volumetric OCT and OCTA to classify eyes into rDR and vtDR. For the scans identified as rDR or vtDR, 3D class activation maps were generated to highlight the subregions which were considered important by the framework for DR classification.

Results

For rDR classification, the framework achieved a 0.96 ± 0.01 area under the receiver operating characteristic curve (AUC) and 0.83 ± 0.04 quadratic-weighted kappa. For vtDR classification, the framework achieved a 0.92 ± 0.02 AUC and 0.73 ± 0.04 quadratic-weighted kappa. In addition, the multiple DR classification (non-rDR, rDR but non-vtDR, or vtDR) achieved a 0.83 ± 0.03 quadratic-weighted kappa.

Conclusions

A deep learning framework only based on OCT and OCTA can provide specialist-level DR classification using only a single imaging modality.

Translational relevance

The proposed framework can be used to develop clinically valuable automated DR diagnosis system because of the specialist-level performance showed in this study.

SUBMITTER: Zang P

PROVIDER: S-EPMC9288155 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography.

Zang Pengxiao P Hormel Tristan T TT Wang Xiaogang X Tsuboi Kotaro K Huang David D Hwang Thomas S TS Jia Yali Y

Translational vision science & technology 20220701 7

<h4>Purpose</h4>Reliable classification of referable and vision threatening diabetic retinopathy (DR) is essential for patients with diabetes to prevent blindness. Optical coherence tomography (OCT) and its angiography (OCTA) have several advantages over fundus photographs. We evaluated a deep-learning-aided DR classification framework using volumetric OCT and OCTA.<h4>Methods</h4>Four hundred fifty-six OCT and OCTA volumes were scanned from eyes of 50 healthy participants and 305 patients with ...[more]

PMID: 35822949

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Project description:PurposeTo evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis.DesignCross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965).ParticipantsPatients with type 1 DM and controls included in the progenitor study.MethodsRadiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types.Main outcome measuresArea under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types.ResultsA dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets.ConclusionsRadiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM.Financial disclosuresProprietary or commercial disclosure may be found after the references.

Project description:PurposeTo evaluate the performance of a federated learning framework for deep neural network-based retinal microvasculature segmentation and referable diabetic retinopathy (RDR) classification using OCT and OCT angiography (OCTA).DesignRetrospective analysis of clinical OCT and OCTA scans of control participants and patients with diabetes.ParticipantsThe 153 OCTA en face images used for microvasculature segmentation were acquired from 4 OCT instruments with fields of view ranging from 2 × 2-mm to 6 × 6-mm. The 700 eyes used for RDR classification consisted of OCTA en face images and structural OCT projections acquired from 2 commercial OCT systems.MethodsOCT angiography images used for microvasculature segmentation were delineated manually and verified by retina experts. Diabetic retinopathy (DR) severity was evaluated by retinal specialists and was condensed into 2 classes: non-RDR and RDR. The federated learning configuration was demonstrated via simulation using 4 clients for microvasculature segmentation and was compared with other collaborative training methods. Subsequently, federated learning was applied over multiple institutions for RDR classification and was compared with models trained and tested on data from the same institution (internal models) and different institutions (external models).Main outcome measuresFor microvasculature segmentation, we measured the accuracy and Dice similarity coefficient (DSC). For severity classification, we measured accuracy, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve, balanced accuracy, F1 score, sensitivity, and specificity.ResultsFor both applications, federated learning achieved similar performance as internal models. Specifically, for microvasculature segmentation, the federated learning model achieved similar performance (mean DSC across all test sets, 0.793) as models trained on a fully centralized dataset (mean DSC, 0.807). For RDR classification, federated learning achieved a mean AUROC of 0.954 and 0.960; the internal models attained a mean AUROC of 0.956 and 0.973. Similar results are reflected in the other calculated evaluation metrics.ConclusionsFederated learning showed similar results to traditional deep learning in both applications of segmentation and classification, while maintaining data privacy. Evaluation metrics highlight the potential of collaborative learning for increasing domain diversity and the generalizability of models used for the classification of OCT data.

Project description:ObjectiveTo evaluate the ability of capillary nonperfusion parameters on OCT angiography (OCTA) to predict the development of clinically significant outcomes in eyes with referable nonproliferative diabetic retinopathy (NPDR).DesignProspective longitudinal observational study.SubjectsIn total, 59 patients (74 eyes) with treatment-naive moderate and severe (referable) NPDR.MethodsPatients were imaged with OCTA at baseline and then followed-up for 1 year. We evaluated 2 OCTA capillary nonperfusion metrics, vessel density (VD) and geometric perfusion deficits (GPDs), in the superficial capillary plexus, middle capillary plexus (MCP), and deep capillary plexus (DCP). We compared the predictive accuracy of baseline OCTA metrics for clinically significant diabetic retinopathy (DR) outcomes at 1 year.Main outcome measuresSignificant clinical outcomes at 1 year, defined as 1 or more of the following-vitreous hemorrhage, center-involving diabetic macular edema, and initiation of treatment with pan-retinal photocoagulation or anti-VEGF injections.ResultsOverall, 49 patients (61 eyes) returned for the 1-year follow-up. Geometric perfusion deficits and VD in the MCP and DCP correlated with clinically significant outcomes at 1 year (P < 0.001). Eyes with these outcomes had lower VD and higher GPD, indicating worse nonperfusion of the deeper retinal layers than those that remained free from complication. These differences remained significant (P = 0.046 to < 0.001) when OCTA parameters were incorporated into models that also considered sex, baseline corrected visual acuity, and baseline DR severity. Adjusted receiver operating characteristic curve for DCP GPD achieved an area under the curve (AUC) of 0.929, with sensitivity of 89% and specificity of 98%. In a separate analysis focusing on high-risk proliferative diabetic retinopathy outcomes, MCP and DCP GPD and VD remained significantly predictive with comparable AUC and sensitivities to the pooled analysis.ConclusionsEvidence of deep capillary nonperfusion at baseline in eyes with clinically referable NPDR can predict short-term DR complications with high accuracy, suggesting that deep retinal ischemia has an important pathophysiologic role in DR progression. Our results suggest that OCTA may provide additional prognostic benefit to clinical DR staging in eyes with high risk.

Project description:To investigate the distribution of clinically significant nonperfusion areas (NPAs) on widefield OCT angiography (OCTA) images in patients with diabetes. Prospective, cross-sectional, observational study. One hundred and forty-four eyes of 114 patients with diabetes. Nominal 20 × 23 mm OCTA images were obtained using a swept-source OCTA device (Xephilio OCT-S1), followed by the creation of en face images 20-mm (1614 pixels) in diameter centering on the fovea. The nonperfusion squares (NPSs) were defined as the 10 × 10 pixel squares without retinal vessels, and the ratio of eyes with the NPSs to all eyes in each square was referred to as the NPS ratio. The areas with probabilistic differences (APD) for proliferative diabetic retinopathy (PDR) and nonproliferative diabetic retinopathy (NPDR) (APD[PDR] and APD[NPDR]) were defined as sets of squares with higher NPS ratios in eyes with PDR and NPDR, respectively. The P ratio (NPSs within APD[PDR] but not APD[NPDR]/all NPSs) was also calculated. The probabilistic distribution of the NPSs and the association with diabetic retinopathy (DR) severity. The NPSs developed randomly in eyes with mild and moderate NPDR and were more prevalent in the extramacular areas and the temporal quadrant in eyes with severe NPDR and PDR. The APD(PDR) was distributed mainly in the extramacular areas, sparing the areas around the vascular arcades and radially peripapillary capillaries. The APD(PDR) contained retinal neovascularization more frequently than the non-APD(PDR) (P = 0.023). The P ratio was higher in eyes with PDR than in those with NPDR (P < 0.001). The multivariate analysis designated the P ratio (odds ratio, 8.293 × 107; 95% confidence interval, 6.529 × 102-1.053 × 1013; P = 0.002) and the total NPSs (odds ratio, 1.002; 95% confidence interval, 1.001-1.003; P < 0.001) as independent risk factors of PDR. Most eyes with NPDR and 4-2-1 rule findings of DR severity had higher P ratios but not necessarily greater NPS numbers. The APD(PDR) is uniquely distributed on widefield OCTA images, and the NPA location patterns are associated with DR severity, independent of the entire area of NPAs. Proprietary or commercial disclosure may be found after the references.

Project description:PurposeTimely 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.DesignCross sectional study.ParticipantsFive 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.MethodsThe 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 measuresThe 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.ResultsFor 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.ConclusionsDeep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases.Financial disclosuresProprietary or commercial disclosure may be found after the references.

Dataset Information

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography.

Purpose

Methods

Results

Conclusions

Translational relevance

Publications

A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography.

Similar Datasets

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