Quantitative evaluation of early retinal changes in children with type 1 diabetes mellitus without retinopathy

Tekin, Kemal; İnanç, Merve; Kurnaz, Erdal; Bayramoğlu, Elvan; Aydemır, Emre; Koç, Mustafa; Kızıltoprak, Hasan; Aycan, Zehra

doi:10.1111/cxo.12667

Cited by 28 publications

(26 citation statements)

References 31 publications

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“…The least amount of significant cells was detected for INL in both studies. These results are in accordance with results of other studies that reported thickness reduction of inner retinal layers in patients with T1DM (e.g., works by the authors of [29,36,37]) or T2DM (e.g., works by the authors of [35,38]) as an early event of neurodegenerative change.…”

Section: Grid-based Analysis Of Retinal Layer Thickness In Patients Wsupporting

confidence: 93%

Enhanced Grid-Based Visual Analysis of Retinal Layer Thickness with Optical Coherence Tomography

et al. 2019

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Optical coherence tomography enables high-resolution 3D imaging of retinal layers in the human eye. The thickness of the layers is commonly assessed to understand a variety of retinal and systemic disorders. Yet, the thickness data are complex and currently need to be considerably reduced prior to further processing and analysis. This leads to a loss of information on localized variations in thickness, which is important for early detection of certain retinal diseases. We propose an enhanced grid-based reduction and exploration of retinal thickness data. Alternative grids are computed, their representation quality is rated, and best fitting grids for given thickness data are suggested. Selected grids are then visualized, adapted, and compared at different levels of granularity. A visual analysis tool bundles all computational, visual, and interactive means in a flexible user interface. We demonstrate the utility of our tool in a complementary analysis procedure, which eases the evaluation of ophthalmic study data. Ophthalmologists successfully applied our solution to study localized variations in thickness of retinal layers in patients with diabetes mellitus.

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Section: Grid-based Analysis Of Retinal Layer Thickness In Patients Wsupporting

confidence: 93%

Enhanced Grid-Based Visual Analysis of Retinal Layer Thickness with Optical Coherence Tomography

et al. 2019

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“…Quantification of perfused retinal capillaries may prove valuable for developing sensitive biomarkers to earlier diagnose and monitor the progression of ocular and systemic diseases. 4,5,[12][13][14] The quantification of capillary parameters typically requires a segmented, binary image. Although manual segmentation of the capillaries in grayscale AOSLO or OCTA images can be performed, such a procedure is largely subjective and requires many hours for completion by a skilled observer.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

Musial

Queener

Adhikari

et al. 2020

Trans. Vis. Sci. Tech.

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Adaptive optics scanning laser ophthalmoscope (AOSLO) capillary perfusion images can possess large variations in contrast, intensity, and background signal, thereby limiting the use of global or adaptive thresholding techniques for automatic segmentation. We sought to develop an automated approach to segment perfused capillaries in AOSLO images. Methods: 12,979 image patches were extracted from manually segmented AOSLO montages from 14 eyes and used to train a convolutional neural network (CNN) that classified pixels as capillaries, large vessels, background, or image canvas. 1764 patches were extracted from AOSLO montages of four separate subjects, and were segmented manually by two raters (ground truth) and automatically by the CNN, an Otsu's approach, and a Frangi approach. A modified Dice coefficient was created to account for slight spatial differences between the same manually and CNN-segmented capillaries. Results: CNN capillary segmentation had an accuracy (0.94), a Dice coefficient (0.67), and a modified Dice coefficient (0.90) that were significantly higher than other automated approaches (P < 0.05). There were no significant differences in capillary density and mean segment length between manual ground-truth and CNN segmentations (P > 0.05). Conclusions: Close agreement between the CNN and manual segmentations enables robust and objective quantification of perfused capillary metrics. The developed CNN is time and computationally efficient, and distinguishes capillaries from areas containing diffuse background signal and larger underlying vessels. Translational Relevance: This automatic segmentation algorithm greatly increases the efficiency of quantifying AOSLO capillary perfusion images.

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“…5,6 Optical coherence tomography (OCT) has shown reduced thickness of the retinal nerve fiber and ganglion cell layers in children with T1DM without retinopathy, which is suggestive of early neuronal damage. [6][7][8] Furthermore, in vivo corneal confocal microscopy (IVCCM), a rapid noninvasive ophthalmic imaging technique has also been shown to identify early corneal cellular and nerve fiber pathology in children and adolescents with T1DM, 9 adults with T1DM without neuropathy, 10 or retinopathy or microalbuminuria. 11 Furthermore, reduced corneal nerve fiber length predicts the development of clinical diabetic neuropathy 12 and the development or worsening of retinopathy.…”

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confidence: 99%

Longitudinal Changes in Corneal Cell and Nerve Fiber Morphology in Young Patients with Type 1 Diabetes with and without Diabetic Retinopathy: A 2-Year Follow-up Study

Deák

Szalai

Tóth

et al. 2019

Invest. Ophthalmol. Vis. Sci.

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PURPOSE. We have previously used in vivo corneal confocal microscopy (IVCCM) to demonstrate significant alterations in the corneal epithelial cells, stromal keratocytes, and subbasal nerves in young patients with type 1 diabetes mellitis (T1DM), especially those with diabetic retinopathy (DR). We have evaluated the change in corneal cellular and subbasal nerve morphology over 2 years in young patients with T1DM with or without DR. METHODS. A total of 19 patients with T1DM, without (n ¼ 12) and with (n ¼ 7) DR and 19 ageand sex-matched healthy control subjects underwent quantification of corneal cellular and subbasal nerve plexus morphology by using IVCCM at baseline and after 2 years. RESULTS. There was no significant change in corneal basal epithelial, posterior stromal keratocyte, or endothelial cell densities over 2 years. However, there was a significant reduction in corneal nerve branch (P ¼ 0.03) and total nerve branch density (P ¼ 0.04) in patients without DR and a significant reduction in corneal nerve fibre density (P ¼ 0.004) in those with DR. CONCLUSIONS. IVCCM can detect a progressive loss of corneal nerve fibers in young patients with T1DM and may allow the identification of individuals at risk of neuropathy progression for more active risk factor reduction.

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Quantitative evaluation of early retinal changes in children with type 1 diabetes mellitus without retinopathy

Abstract: Retinal neural changes, which can be shown by SD-OCT, may be present in diabetic eyes even before clinically detectable retinal vasculopathy. Macular and RNFL thickness measurements might be useful indicators for early detection of diabetic retinopathy in the future.

Cited by 28 publications

References 31 publications

Enhanced Grid-Based Visual Analysis of Retinal Layer Thickness with Optical Coherence Tomography

Enhanced Grid-Based Visual Analysis of Retinal Layer Thickness with Optical Coherence Tomography

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network

Longitudinal Changes in Corneal Cell and Nerve Fiber Morphology in Young Patients with Type 1 Diabetes with and without Diabetic Retinopathy: A 2-Year Follow-up Study

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