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Role of Inflammation in Classification of Diabetic Macular Edema by Optical Coherence Tomography.

ABSTRACT: Diabetic macular edema (DME) is the abnormal accumulation of fluid in the subretinal or intraretinal spaces in the macula in patients with diabetic retinopathy and leads to severely impaired central vision. Technical developments in retinal imaging systems have led to many advances in the study of DME. In particular, optical coherence tomography (OCT) can provide longitudinal and microstructural analysis of the macula. A comprehensive review was provided regarding the role of inflammation using OCT-based classification of DME and current and ongoing therapeutic approaches. In this review, we first describe the pathogenesis of DME, then discuss the classification of DME based on OCT findings and the association of different types of DME with inflammation, and finally describe current and ongoing therapeutic approaches using OCT-based classification of DME. Inflammation has an important role in the pathogenesis of DME, but its role appears to differ among the DME phenotypes, as determined by OCT. It is important to determine how the different DME subtypes respond to intravitreal injections of steroids, antivascular endothelial growth factor agents, and other drugs to improve prognosis and responsiveness to treatment.

SUBMITTER: Chung YR

PROVIDER: S-EPMC6939426 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Role of Inflammation in Classification of Diabetic Macular Edema by Optical Coherence Tomography.

Chung Yoo-Ri YR Kim Young Ho YH Ha Seong Jung SJ Byeon Hye-Eun HE Cho Chung-Hyun CH Kim Jeong Hun JH Lee Kihwang K

Journal of diabetes research 20191220

Diabetic macular edema (DME) is the abnormal accumulation of fluid in the subretinal or intraretinal spaces in the macula in patients with diabetic retinopathy and leads to severely impaired central vision. Technical developments in retinal imaging systems have led to many advances in the study of DME. In particular, optical coherence tomography (OCT) can provide longitudinal and microstructural analysis of the macula. A comprehensive review was provided regarding the role of inflammation using ...[more]

PMID: 31930145

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Project description:Several studies published so far used highly selective image datasets from unclear sources to train computer vision models and that may lead to overestimated results, while those studies conducted in real-life remain scarce. To avoid image selection bias, we stacked convolutional and recurrent neural networks (CNN-RNN) to analyze complete optical coherence tomography (OCT) cubes in a row and predict diabetic macular edema (DME), in a real-world diabetic retinopathy screening program. A retrospective cohort study was carried out. Throughout 4-years, 5314 OCT cubes from 4408 subjects who attended to the diabetic retinopathy (DR) screening program were included. We arranged twenty-two (22) pre-trained CNNs in parallel with a bidirectional RNN layer stacked at the bottom, allowing the model to make a prediction for the whole OCT cube. The staff of retina experts built a ground truth of DME later used to train a set of these CNN-RNN models with different configurations. For each trained CNN-RNN model, we performed threshold tuning to find the optimal cut-off point for binary classification of DME. Finally, the best models were selected according to sensitivity, specificity, and area under the receiver operating characteristics curve (AUROC) with their 95% confidence intervals (95%CI). An ensemble of the best models was also explored. 5188 cubes were non-DME and 126 were DME. Three models achieved an AUROC of 0.94. Among these, sensitivity, and specificity (95%CI) ranged from 84.1-90.5 and 89.7-93.3, respectively, at threshold 1, from 89.7-92.1 and 80-83.1 at threshold 2, and from 80.2-81 and 93.8-97, at threshold 3. The ensemble model improved these results, and lower specificity was observed among subjects with sight-threatening DR. Analysis by age, gender, or grade of DME did not vary the performance of the models. CNN-RNN models showed high diagnostic accuracy for detecting DME in a real-world setting. This engine allowed us to detect extra-foveal DMEs commonly overlooked in other studies, and showed potential for application as the first filter of non-referable patients in an outpatient center within a population-based DR screening program, otherwise ended up in specialized care.

Project description:BackgroundDiabetic macular edema (DME) is a major cause of vision loss in diabetics that is currently mainly treated by antivascular endothelial growth factor (VEGF) agents. The effect of these agents on macular perfusion (MP) is a current concern. Optical coherence tomography angiography (OCTA) is an imaging modality that allows noninvasive high-resolution retinal microvasculature imaging. Several recent studies evaluated the effect of anti-VEGF agents on the MP of DME patients using OCTA. Our aim is to provide a systematic review of these studies.MethodsMultiple databases were searched including PubMed, Ovid Medline, EMBASE, and Google Scholar for relevant studies published between January 2016 and November 2020 which were included in this review. Studies were compared regarding their design, the number of included patients, the machine and scanning protocol used, the inclusion and exclusion criteria, the number of injections given, the type of anti-VEGF agent used, the outcome measures assessed, and the effect of injections on different MP parameters.ResultsA total of 16 studies were included. The studies assessed various OCTA parameters that define MP including the foveal avascular zone area and superficial and deep vascular density and yielded conflicting results. Seven studies showed stable or improved MP following treatment, while 7 studies showed worsening MP following treatment, and 2 studies showed inconclusive results. This could have been due to differences in study design, inclusion criteria, type of anti-VEGF agents used, treatment duration, and methods of image analysis and vascular density quantification. All identified studies were noncomparative case series, and 14 of them (87.5%) used the RTVue XR Avanti OCTA machine. Only one study compared OCTA to fluorescein angiography findings.ConclusionAnalysis of MP changes following VEGF inhibition for DME could benefit from a unified scanning protocol and analysis approach that uses similar study designs to eliminate potential sources of bias. This may provide more definitive conclusions regarding the effect of treatment on MP.

Project description:ObjectiveTo evaluate agreement between fluorescein angiography (FA) and optical coherence tomography (OCT) results for diagnosis of macular edema in patients with uveitis.DesignMulticenter cross-sectional study.ParticipantsFour hundred seventy-nine eyes with uveitis from 255 patients.MethodsThe macular status of dilated eyes with intermediate uveitis, posterior uveitis, or panuveitis was assessed via Stratus-3 OCT and FA. To evaluate agreement between the diagnostic approaches, ? statistics were used.Main outcome measuresMacular thickening (MT; center point thickness, ? 240 ?m per reading center grading of OCT images) and macular leakage (ML; central subfield fluorescein leakage, ? 0.44 disc areas per reading center grading of FA images), and agreement between these outcomes in diagnosing macular edema.ResultsOptical coherence tomography (90.4%) more frequently returned usable information regarding macular edema than FA (77%) or biomicroscopy (76%). Agreement in diagnosis of MT and ML (? = 0.44) was moderate. Macular leakage was present in 40% of cases free of MT, whereas MT was present in 34% of cases without ML. Biomicroscopic evaluation for macular edema failed to detect 40% and 45% of cases of MT and ML, respectively, and diagnosed 17% and 17% of cases with macular edema that did not have MT or ML, respectively; these results may underestimate biomicroscopic errors (ophthalmologists were not explicitly masked to OCT and FA results). Among eyes free of ML, phakic eyes without cataract rarely (4%) had MT. No factors were found that effectively ruled out ML when MT was absent.ConclusionsOptical coherence tomography and FA offered only moderate agreement regarding macular edema status in uveitis cases, probably because what they measure (MT and ML) are related but nonidentical macular pathologic characteristics. Given its lower cost, greater safety, and greater likelihood of obtaining usable information, OCT may be the best initial test for evaluation of suspected macular edema. However, given that ML cannot be ruled out if MT is absent and vice versa, obtaining the second test after negative results on the first seems justified when detection of ML or MT would alter management. Given that biomicroscopic evaluation for macular edema erred frequently, ancillary testing for macular edema seems indicated when knowledge of ML or MT status would affect management.Financial disclosure(s)Proprietary or commercial disclosure may be found after the references.

Project description:Given the capacity of Optical Coherence Tomography (OCT) imaging to display structural changes in a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called "Livelayer" which is designed for straightforward segmentation of retinal layers and fluids. This method is chiefly based on Dijkstra's Shortest Path First (SPF) algorithm and the Live-wire function together with some preprocessing operations on the to-be-segmented images. The software is indeed suitable for obtaining detailed segmentation of layers, exact localization of clear or unclear fluid objects and the ground truth, demanding far less endeavor in comparison to a common manual segmentation method. It is also valuable as a tool for calculating the irregularity index in deformed OCT images. The amount of time (seconds) that Livelayer required for segmentation of Inner Limiting Membrane, Inner Plexiform Layer-Inner Nuclear Layer, Outer Plexiform Layer-Outer Nuclear Layer was much less than that for the manual segmentation, 5 s for the ILM (minimum) and 15.57 s for the OPL-ONL (maximum). The unsigned errors (pixels) between the semi-automatically labeled and gold standard data was on average 2.7, 1.9, 2.1 for ILM, IPL-INL, OPL-ONL, respectively. The Bland-Altman plots indicated perfect concordance between the Livelayer and the manual algorithm and that they could be used interchangeably. The repeatability error was around one pixel for the OPL-ONL and < 1 for the other two. The unsigned errors between the Livelayer and the manual algorithm was 1.33 for ILM and 1.53 for Nerve Fiber Layer-Ganglion Cell Layer in peripapillary B-Scans. The Dice scores for comparing the two algorithms and for obtaining the repeatability on segmentation of fluid objects were at acceptable levels.

Dataset Information

Role of Inflammation in Classification of Diabetic Macular Edema by Optical Coherence Tomography.

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Role of Inflammation in Classification of Diabetic Macular Edema by Optical Coherence Tomography.

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