Disorganization of Retinal Inner Layers (DRIL) and Neuroretinal Dysfunction in Early Diabetic Retinopathy

Joltikov, Katherine A.; Sesi, Christopher; Castro, Vivian Carla de; Dávila, José; Anand, Rohit; Khan, Sami M.; Farbman, Neil; Jackson, Gregory R.; Johnson, Chris; Gardner, Thomas W.

doi:10.1167/iovs.18-24955

Cited by 72 publications

(49 citation statements)

References 30 publications

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“…A very recent work of Joltikov et al. (2018) elucidated the relationship between disorganization of retinal inner layers (DRILs) and retinal function in diabetic patients without diabetic retinopathy (DR) and with nonproliferative DR, but without diabetic macular edema (DME), using ETDRS visual acuity, the quick contrast sensitivity function, short-wavelength automated perimetry (SWAP), standard automated perimetry (SAP), and frequency doubling perimetry (FDP).…”

Section: Discussionmentioning

confidence: 99%

Visual contrast sensitivity could be an early marker of diabetic retinopathy

Pramanik

Chowdhury

Ganguly

et al. 2020

Heliyon

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The present study aimed to explore the early predictive marker of diabetic retinopathy (DR) and to elucidate the associated demographic, metabolic, and ocular factors. We enrolled 43 type 2 diabetic subjects with mild non-proliferative retinopathy (MNPDR), 30 diabetic subjects with no retinopathy (DNR), and 35 healthy controls (HC). The study groups showed no significant alteration in central macular thickness (CMT) and visual acuity (VA). The contrast sensitivity (CS) score was found to be significantly lower among DNR and MNPDR subjects compared to HCs (p < 0.0001). Between MNPDR and DNR subjects, the CS score was significantly lower in the former (p = 0.0036). CS score discriminated DNR subjects from HC, with 74% accuracy for the optimal threshold 0.71. The associated area under the ROC curve (AUC) is 0.82 (p < 0.0001) while the discrimination rule has 66% sensitivity and 80% specificity. The CS score also discriminated MNPDR subjects from DNR with 64% accuracy for the optimal threshold 0.53. The associated AUC is 0.65 (p < 0.023) and the rule has 86% sensitivity and 33% specificity. According to best subset regression analysis, not only glycaemic parameters but also lipid parameters [low-density lipoprotein cholesterol (LDL-C) (p = 0.045) and triglycerides (TG) (p = 0.0005)] were found to be significant predictors of CS. CMT (p = 0.058) was another marginally significant predictor of CS. CS may be used as an early predictive marker for DR. So, not only hyperglycemia, but also hyperlipidemia seems to significantly affect retinal CS function in diabetes.

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Section: Discussionmentioning

confidence: 99%

Visual contrast sensitivity could be an early marker of diabetic retinopathy

Pramanik

Chowdhury

Ganguly

et al. 2020

Heliyon

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“…This is validated from our results due to the discriminative capability and the p-values corresponding to INL in the comparison between controls and NPDR. Also, disorganization of retinal inner layers (DRILs) is associated with inner retinal thinning in patients with NoDR 10,21 . When comparing between NPDR and NoDR or controls, inner nuclear, outer plexiform, and ganglion cell layers were better predictors.…”

Section: Discussionmentioning

confidence: 99%

Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography

Mohammed

Chen

et al. 2020

Sci Rep

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Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are affected in patients with DR. However, most studies analyzed the thickness by calculating summary statistics from retinal thickness maps of the macula region. This study aims to apply a density function-based statistical framework to the thickness data obtained through OCT, and to compare the predictive power of various retinal layers to assess the severity of DR. We used a prototype data set of 107 subjects which are comprised of 38 non-proliferative DR (NPDR), 28 without DR (NoDR), and 41 controls. Based on the thickness profiles, we constructed novel features which capture the variation in the distribution of the pixel-wise retinal layer thicknesses from OCT. We quantified the predictive power of each of the retinal layers to distinguish between all three pairwise comparisons of the severity in DR (NoDR vs NPDR, controls vs NPDR, and controls vs NoDR). When applied to this preliminary DR data set, our density-based method demonstrated better predictive results compared with simple summary statistics. Furthermore, our results indicate considerable differences in retinal layer structuring based on the severity of DR. We found that: (a) the outer plexiform layer is the most discriminative layer for classifying NoDR vs NPDR; (b) the outer plexiform, inner nuclear and ganglion cell layers are the strongest biomarkers for discriminating controls from NPDR; and (c) the inner nuclear layer distinguishes best between controls and NoDR.

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“…While pathologies such as subretinal fluid are known to alter the appearance of the retina, some other are strictly related with the layers (e.g. the disorganization of the retinal inner layers, or DRIL) [23]. Therefore, using retinal layer information is an appropriate way of incorporating anatomical knowledge into the model.…”

Section: A Retinal Oct Imagingmentioning

confidence: 99%

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

Seeböck

Orlando

Schlegl

et al. 2020

IEEE Trans. Med. Imaging

113

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Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological areas, it is limited by requiring a-priori definitions of these regions, large-scale annotations, and a representative patient cohort in the training set. In contrast, anomaly detection is not limited to specific definitions of pathologies and allows for training on healthy samples without annotation. Anomalous regions can then serve as candidates for biomarker discovery. Knowledge about normal anatomical structure brings implicit information for detecting anomalies. We propose to take advantage of this property using bayesian deep learning, based on the assumption that epistemic uncertainties will correlate with anatomical deviations from a normal training set. A Bayesian U-Net is trained on a well-defined healthy environment using weak labels of healthy anatomy produced by existing methods. At test time, we capture epistemic uncertainty estimates of our model using Monte Carlo dropout. A novel post-processing technique is then applied to exploit these estimates and transfer their layered appearance to smooth blob-shaped segmentations of the anomalies. We experimentally validated this approach in retinal optical coherence tomography (OCT) images, using weak labels of retinal layers. Our method achieved a Dice index of 0.789 in an independent anomaly test set of age-related macular degeneration (AMD) cases. The resulting segmentations allowed very high accuracy for separating healthy and diseased cases with late wet AMD, dry geographic atrophy (GA), diabetic macular edema (DME) and retinal vein occlusion (RVO). Finally, we qualitatively observed that our approach can also detect other deviations in normal scans such as cut edge artifacts.

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Disorganization of Retinal Inner Layers (DRIL) and Neuroretinal Dysfunction in Early Diabetic Retinopathy

Cited by 72 publications

References 30 publications

Visual contrast sensitivity could be an early marker of diabetic retinopathy

Visual contrast sensitivity could be an early marker of diabetic retinopathy

Density-based classification in diabetic retinopathy through thickness of retinal layers from optical coherence tomography

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT

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