State of the art spatial visualization of the response of neovascularisation to anti-vascular endothelial growth factor therapy

PURPOSE: To visualize the mode of action of anti-vascular endothelial growth factor (anti-VEGFs) therapy on retinal neovascularization (RNV) in a patient with macular telangiectasia (MacTel) type 2 using a detailed three-dimensional data environment. OBSERVATION: A 60-year-old man presented with visual acuity loss and was diagnosed with MacTel type 2. Fluorescein angiography was not possible for safety reasons because of a history of severe reaction to fluorescein dye at his referring hospital. Optical coherence tomography angiography (OCTA) imaging revealed new retinal neovascular membranes (RNV) in the macula of both eyes. A marked reduction in the size of the RNV in both eyes was evident on volume-rendered three-dimensional OCTA retinal imaging after the first anti-VEGF injection. CONCLUSION AND IMPORTANCE: The ability to directly observe the effect of anti-VEGF injections on a RNV using three-dimensional OCTA was successfully demonstrated. This can be useful in patients with previous allergic and potentially lethal complications to fluorescein. In addition, enhanced three-dimensional spatial display of RNV leads to a greater understanding of the perfusion profile and the anatomical changes that occur in ocular neovascularization relative to surrounding tissue. This has the potential to provide insight into the pathobiology of angiogenesis

Progression of Retinopathy Secondary to Maternally Inherited Diabetes and Deafness – Evaluation of Predicting Parameters

PURPOSE: To investigate the prognostic value of demographic, functional, and imaging parameters on retinal pigment epithelium (RPE) atrophy progression secondary to Maternally Inherited Diabetes and Deafness (MIDD) and to evaluate the application of these factors in clinical trial design. DESIGN: Retrospective observational case series. METHODS: Thirty-five eyes of 20 patients (age range, 24.9-75.9 years) with genetically proven MIDD and demarcated RPE atrophy on serial fundus autofluorescence (AF) images were included. Lesion size and shape-descriptive parameters were longitudinally determined by two independent readers. A linear mixed effect model was used to predict the lesion enlargement rate based on baseline variables. Sample size calculations were performed to model the power in a simulated interventional study. RESULTS: The mean follow-up time was 4.27 years. The mean progression rate of RPE atrophy was 2.33 mm2/year revealing a dependence on baseline lesion size (+0.04 [0.02-0.07] mm2/year/mm2, p<0.001), which was absent after square root transformation. The fovea was preserved in the majority of patients during the observation time. In the case of foveal involvement, the loss of visual acuity lagged behind central RPE atrophy in AF images. Sex, age, and number of atrophic foci predicted future progression rates with a cross-validated mean absolute error of 0.13 mm/year and to reduce the required sample size for simulated interventional trials. CONCLUSIONS: Progressive RPE atrophy could be traced in all eyes using AF imaging. Shape-descriptive factors and patients' baseline characteristics had significant prognostic value, guiding appropriate subject selection and sample size in future interventional trial design

Cynomolgus monkey's choroid reference database derived from hybrid deep learning optical coherence tomography segmentation.

Cynomolgus monkeys exhibit human-like features, such as a fovea, so they are often used in non-clinical research. Nevertheless, little is known about the natural variation of the choroidal thickness in relation to origin and sex. A combination of deep learning and a deterministic computer vision algorithm was applied for automatic segmentation of foveolar optical coherence tomography images in cynomolgus monkeys. The main evaluation parameters were choroidal thickness and surface area directed from the deepest point on OCT images within the fovea, marked as the nulla with regard to sex and origin. Reference choroid landmarks were set underneath the nulla and at 500 µm intervals laterally up to a distance of 2000 µm nasally and temporally, complemented by a sub-analysis of the central bouquet of cones. 203 animals contributed 374 eyes for a reference choroid database. The overall average central choroidal thickness was 193 µm with a coefficient of variation of 7.8%, and the overall mean surface area of the central bouquet temporally was 19,335 µm2 and nasally was 19,283 µm2. The choroidal thickness of the fovea appears relatively homogeneous between the sexes and the studied origins. However, considerable natural variation has been observed, which needs to be appreciated

Volume-rendered optical coherence tomography angiography during ocular interventions: Advocating for noninvasive intraoperative retinal perfusion monitoring.

We aimed to test for feasibility of volume-rendered optical coherence tomography angiography (OCTA) as a novel method for assessing/quantifying retinal vasculature during ocular procedures and to explore the potential for intraoperative use. Thirty patients undergoing periocular anaesthesia were enrolled, since published evidence suggests a reduction in ocular blood flow. Retinal perfusion was monitored based on planar OCTA image-derived data provided by a standard quantification algorithm and postprocessed/volume-rendered OCTA data using a custom software script. Overall, imaging procedures were successful, yet imaging artifacts occurred frequently. In interventional eyes, perfusion parameters decreased during anaesthesia. Planar image-derived and volume rendering-derived parameters were correlated. No correlation was found between perfusion parameters and a motion artifact score developed for this study, yet all perfusion parameters correlated with signal strength as displayed by the device. Concluding, volume-rendered OCTA allows for noninvasive three-dimensional retinal vasculature assessment/quantification in challenging surgical settings and appears generally feasible for intraoperative use

Validation of automated artificial intelligence segmentation of optical coherence tomography images

PURPOSE To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. METHODS A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. RESULTS The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison. CONCLUSION The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders

Validation of automated artificial intelligence segmentation of optical coherence tomography images

Purpose To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. Methods A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. Results The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders’ compartmentalization was higher than the mean score for intra-grader group comparison. Conclusion The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders

Safety and Feasibility of a Novel Sparse Optical Coherence Tomography Device for Patient-Delivered Retina Home Monitoring

Purpose To study a novel and fast optical coherence tomography (OCT) device for home-based monitoring in age-related macular degeneration (AMD) in a small sample yielding sparse OCT (spOCT) data and to compare the device to a commercially available reference device. Methods In this prospective study, both eyes of 31 participants with AMD were included. The subjects underwent scanning with an OCT prototype and a spectral-domain OCT to compare the accuracy of the central retinal thickness (CRT) measurements. Results Sixty-two eyes in 31 participants (21 females and 10 males) were included. The mean age was 79.6 years (age range, 69-92 years). The mean difference in the CRT measurements between the devices was 4.52 μm (SD ± 20.0 μm; range, -65.6 to 41.5 μm). The inter- and intrarater reliability coefficients of the OCT prototype were both >0.95. The laser power delivered was <0.54 mW for spOCT and <1.4 mW for SDOCT. No adverse events were reported, and the visual acuity before and after the measurements was stable. Conclusion This study demonstrated the safety and feasibility of this home-based OCT monitoring under real-life conditions, and it provided evidence for the potential clinical benefit of the device. Translational Relevance The newly developed spOCT is a valid and readily available retina scanner. It could be applied as a portable self-measuring OCT system. Its use may facilitate the sustainable monitoring of chronic retinal diseases by providing easily accessible and continuous retinal monitoring

Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

Machine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization ('neural recording'). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications