Ultra-wide-field Pseudo-color Research Articles

Reliability of Clinical Grading of Diabetic Retinopathy Compared to Grading of Ultra-widefield Images

Purpose: Evaluate the reliability of clinical grading of diabetic retinopathy (DR) severity compared to grading on ultra-widefield pseudocolor fundus (UWF-CF) and ultra-widefield fluorescein angiography (UWF-FA) images and their relative detection of sight-threatening DR (STDR) and referable DR. Methods: We analyzed 184 diabetic eyes. UWF-CF and UWF-FA images were graded based on the International Clinical Diabetic Retinopathy (ICDR) severity scale. Agreement between clinical and UWF-based severity grading was evaluated using Cohen’s kappa coefficient. The rate of STDR and referable DR was evaluated for each grading method. Results: We found moderate agreement between clinical grading and UWF-CF (k=.456, p <.001) and between UWF-CF and UWF-FA (k= .443, p <.001). Agreement between clinical grading and UWF-FA was fair (k=.397, p <.001). UWF-based grading identified a higher DR grade in 56 eyes (30%) on UWF-CF and 85 eyes (46.2%) on UWF-FA. Compared to clinical grading, UWF-FA detected a higher rate of STDR (44%; 81/184 vs 22.3%; 41/184), while UWF-CF detected more referable eyes (58.1%;107/184 vs 45.65%;84/184). Conclusion: UWF-CF is a valuable tool for identifying referable eyes and can be a useful, non-invasive adjunct to clinical grading. Our results suggest that UWF-FA is particularly useful for detecting unsuspected STDR in eyes with clinically referable DR.

Assessing Diabetic Retinopathy Staging With AI: A Comparative Analysis Between Pseudocolor and LED Imaging.

To compare the diagnostic performance of artificial intelligence (AI)-based diabetic retinopathy (DR) staging system across pseudocolor, simulated white light (SWL), and light-emitting diode (LED) camera imaging modalities. A cross-sectional investigation involved patients with diabetes undergoing imaging with an iCare DRSplus confocal LED camera and an Optos confocal, ultra-widefield pseudocolor camera, with and without SWL. Macula-centered and optic nerve-centered 45 × 45-degree photographs were processed using EyeArt v2.1. Human graders established the ground truth (GT) for DR severity on dilated fundus exams. Sensitivity and weighted Cohen's weighted kappa (wκ) were calculated. An ordinal generalized linear mixed model identified factors influencing accurate DR staging. The study included 362 eyes from 189 patients. The LED camera excelled in identifying sight-threatening DR stages (sensitivity=0.83, specificity=0.95 for proliferative DR) and had the highest agreement with the GT (wκ=0.71). The addition of SWL to pseudocolor imaging resulted in decreased performance (sensitivity=0.33, specificity=0.98 for proliferative DR; wκ=0.55). Peripheral lesions reduced the likelihood of being staged in the same or higher DR category by 80% (P<0.001). Pseudocolor and LED cameras, although proficient, demonstrated non-interchangeable performance, with the LED camera exhibiting superior accuracy in identifying advanced DR stages. These findings underscore the importance of implementing AI systems trained for ultra-widefield imaging, considering the impact of peripheral lesions on correct DR staging. This study underscores the need for artificial intelligence-based systems specifically trained for ultra-widefield imaging in diabetic retinopathy assessment.

Prevalence of peripheral retinal findings in retinal patients using ultra-widefield pseudocolor fundus imaging

Ultra-widefield retinal imaging is increasingly used in ophthalmology and optometry practices to image patients identifying peripheral abnormalities. However, the clinical relevance of these peripheral retinal abnormalities is unclear. This cross-sectional study aims to firstly validate a new grading system, secondly, assess the prevalence of peripheral retinal abnormalities in retinal patients, and finally understand how peripheral findings may associate with retinal disease. Ultra-widefield pseudocolor fundus images were taken from the eyes of clinic patients. Demographic data and clinical diagnosis for each patient was noted. The grading system was validated using masked retinal specialists. Logistic regression identified associations between retinal disease and peripheral retinal findings. Using the grading system, inter-observer agreement was 76.1% with Cohen’s Kappa coefficient 0.542 (p < 0.0001) and the test–retest agreement was 95.1% with Kappa 0.677(p < 0.0001). 971 images were included, with 625 eyes (64.4%) having peripheral abnormalities. Peripheral drusen was the most common abnormality (n = 221, 22.76%) and correlated with age-related macular degeneration (p < 0.001). Novel correlations were also identified between diabetic retinopathy and retinal pigmentation as well as pigmentary degeneration. This study provides a validated system for identifying peripheral abnormalities and adds to literature highlighting peripheral retinal associations with retinal disease which would benefit from further study.

Circumscribed Choroidal Hemangioma Simulating Choroidal Melanoma on Advanced Ultrawide-Field Pseudocolor Retinal Imaging: A Case Series

The purpose of this case series was to evaluate the features of circumscribed choroidal hemangioma on pseudocolor ultrawide-field (UWF) retinal images simulating choroidal melanoma and compare it to fundoscopic appearance. All four patients underwent full ophthalmological examination, including dilated fundus examination, ultrasonography, and UWF imaging (UWFI). All circumscribed choroidal hemangioma appeared clinically as orange-red choroidal lesions which were echodense with regular internal structure on ultrasonography. All lesions appeared green-grey in color on pseudocolor UWFI. Pseudocolor UWFI of circumscribed choroidal hemangioma represents distortion of true color appearance and may simulate choroidal melanoma. [Ophthalmic Surg Lasers Imaging Retina 2023;54:292-296.].

Estimation of Visual Function Using Deep Learning From Ultra-Widefield Fundus Images of Eyes With Retinitis Pigmentosa

There is no widespread effective treatment to halt the progression of retinitis pigmentosa. Consequently, adequate assessment and estimation of residual visual function are important clinically. To examine whether deep learning can accurately estimate the visual function of patients with retinitis pigmentosa by using ultra-widefield fundus images obtained on concurrent visits. Data for this multicenter, retrospective, cross-sectional study were collected between January 1, 2012, and December 31, 2018. This study included 695 consecutive patients with retinitis pigmentosa who were examined at 5 institutions. Each of the 3 types of input images-ultra-widefield pseudocolor images, ultra-widefield fundus autofluorescence images, and both ultra-widefield pseudocolor and fundus autofluorescence images-was paired with 1 of the 31 types of ensemble models constructed from 5 deep learning models (Visual Geometry Group-16, Residual Network-50, InceptionV3, DenseNet121, and EfficientNetB0). We used 848, 212, and 214 images for the training, validation, and testing data, respectively. All data from 1 institution were used for the independent testing data. Data analysis was performed from June 7, 2021, to December 5, 2022. The mean deviation on the Humphrey field analyzer, central retinal sensitivity, and best-corrected visual acuity were estimated. The image type-ensemble model combination that yielded the smallest mean absolute error was defined as the model with the best estimation accuracy. After removal of the bias of including both eyes with the generalized linear mixed model, correlations between the actual values of the testing data and the estimated values by the best accuracy model were examined by calculating standardized regression coefficients and P values. The study included 1274 eyes of 695 patients. A total of 385 patients were female (55.4%), and the mean (SD) age was 53.9 (17.2) years. Among the 3 types of images, the model using ultra-widefield fundus autofluorescence images alone provided the best estimation accuracy for mean deviation, central sensitivity, and visual acuity. Standardized regression coefficients were 0.684 (95% CI, 0.567-0.802) for the mean deviation estimation, 0.697 (95% CI, 0.590-0.804) for the central sensitivity estimation, and 0.309 (95% CI, 0.187-0.430) for the visual acuity estimation (all P < .001). Results of this study suggest that the visual function estimation in patients with retinitis pigmentosa from ultra-widefield fundus autofluorescence images using deep learning might help assess disease progression objectively. Findings also suggest that deep learning models might monitor the progression of retinitis pigmentosa efficiently during follow-up.

RPGRIP1 variant associated with pigmented paravenous chorioretinal atrophy.

To report a case of Pigmented Paravenous Chorioretinal Atrophy (PPCRA) associated with a novel RPGRIP1 dominant variant. Case report. The patient underwent multimodal retinal imaging, including spectral-domain optical coherence tomography (OCT), OCT Angiography (OCTA), blue-light autofluorescence (BAF), and ultra-widefield pseudocolor retinography and autofluorescence. Genetic testing was performed using next-generation sequencing. A 67-year-old male presented with a clinical suspicion of retinitis pigmentosa. His best-corrected visual acuity was 20/32 in the right eye and 20/200 in the left eye. On fundus examination, paravenous pigment clumping and chorioretinal atrophy were seen bilaterally, matching confluent hypoautofluorescent areas departing from the optic disc. This clinical presentation suggested a case of PPCRA. Genetic testing found a heterozygous deletion of nucleotide 631 (c.631del) in the RPGRIP1 gene, a frameshift variant that generates a premature stop codon (p.Ser211Valfs*64) and therefore results in a truncated or absent protein product. The variant was regarded as likely pathogenic (class IV). In this report, we describe a case of PPCRA in association with a novel, likely pathogenic c.631del, p.Ser211Valfs*64 variant in RPGRIP1, a gene that has been associated with Leber congenital amaurosis and cone-rod dystrophy. Our case expands the spectrum of genes associated with PPCRA and prompts further studies to ascertain the molecular etiopathogenesis of this disease.

MULTI-MODAL IMAGING IN DIFFUSE SUBRETINAL FIBROSIS WITH UVEITIS SYNDROME.

To describe clinical features including multimodal imaging in a case of diffuse subretinal fibrosis and uveitis syndrome. A young patient presented with counting fingers in both eyes due to posterior uveitis and unresponsive to systemic corticosteroids. Ultra-wide field pseudo-color and autofluorescence imaging, optical coherence tomography angiography (OCT-A), en-face OCT-A, and spectral-domain optical coherence were performed. Clinical examination revealed a bilateral fibrosis plaque in both fovea and macular multifocal choroidal lesions associated, with no vitritis. Ultra-wide field-autofluorescence allowed us to determine activity showing a hyperfluorescent pattern. En-face OCT-A at the level of the ellipsoid layer showed multiple hyperreflective lesions surrounding a hyperreflective fibrosis plaque in both eyes, which improved and stabilized during the follow-up. Optical coherence tomography angiography at the level of retina pigmentary epithelium-choriocapillaris revealed neocapillary network changes along the chronification of the disease. The patient required the addition of intravitreal dexamethasone implants (Ozurdex) to control the acute rebounds and anti-TNFα to control the underlying inflammatory process. This is the first diffuse subretinal fibrosis with uveitis syndrome reported with a complete En-face OCT-A and OCT-A examination. This multi-modal imaging will improve the monitoring of the disease's activity and the chronicity changes.

Retinal Dialysis Revealed by Swept-Source OCT

We present a case of a peripheral retinal dialysis treated with laser photocoagulation imaged using far peripheral swept-source (SS)-OCT (Optos Silverstone, Optos). A, Ultra-widefield pseudocolor fundus photography of the left eye shows the inferotemporal retinal dialysis with the pigmented scars corresponding to the previous barricade laser treatment. Green lines (B and C) indicate the locations scanned by SS-OCT. B-scans (B) and (C) demonstrate vitreoretinal traction (arrowhead) with retinal elevation, the presence of intraretinal cystoid spaces and complete absence of the retina more distally (asterisk), consistent with a dialysis (Magnified version of Fig A-C is available online at www.aaojournal.org).

Prediction of age and brachial-ankle pulse-wave velocity using ultra-wide-field pseudo-color images by deep learning

This study examined whether age and brachial-ankle pulse-wave velocity (baPWV) can be predicted with ultra-wide-field pseudo-color (UWPC) images using deep learning (DL). We examined 170 UWPC images of both eyes of 85 participants (40 men and 45 women, mean age: 57.5 ± 20.9 years). Three types of images were included (total, central, and peripheral) and analyzed by k-fold cross-validation (k = 5) using Visual Geometry Group-16. After bias was eliminated using the generalized linear mixed model, the standard regression coefficients (SRCs) between actual age and baPWV and predicted age and baPWV from the UWPC images by the neural network were calculated, and the prediction accuracies of the DL model for age and baPWV were examined. The SRC between actual age and predicted age by the neural network was 0.833 for all images, 0.818 for central images, and 0.649 for peripheral images (all P < 0.001) and between the actual baPWV and the predicted baPWV was 0.390 for total images, 0.419 for central images, and 0.312 for peripheral images (all P < 0.001). These results show the potential prediction capability of DL for age and vascular aging and could be useful for disease prevention and early treatment.

Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images.

Evaluating the discrimination ability of a deep convolution neural network for ultrawide-field pseudocolor imaging and ultrawide-field autofluorescence of retinitis pigmentosa. In total, the 373 ultrawide-field pseudocolor and ultrawide-field autofluorescence images (150, retinitis pigmentosa; 223, normal) obtained from the patients who visited the Department of Ophthalmology, Tsukazaki Hospital were used. Training with a convolutional neural network on these learning data objects was conducted. We examined the K-fold cross validation (K = 5). The mean area under the curve of the ultrawide-field pseudocolor group was 0.998 (95% confidence interval (CI) [0.9953–1.0]) and that of the ultrawide-field autofluorescence group was 1.0 (95% CI [0.9994–1.0]). The sensitivity and specificity of the ultrawide-field pseudocolor group were 99.3% (95% CI [96.3%–100.0%]) and 99.1% (95% CI [96.1%–99.7%]), and those of the ultrawide-field autofluorescence group were 100% (95% CI [97.6%–100%]) and 99.5% (95% CI [96.8%–99.9%]), respectively. Heatmaps were in accordance with the clinician’s observations. Using the proposed deep neural network model, retinitis pigmentosa can be distinguished from healthy eyes with high sensitivity and specificity on ultrawide-field pseudocolor and ultrawide-field autofluorescence images.

Role of Ultra-Wide Field Imaging in the Management of Tubercular Posterior Uveitis

ABSTRACTPurpose: To assess the utility of ultra-wide field (UWF) versus conventional fundus imaging in the management of tubercular (TB) posterior uveitis.Methods: Twenty-two consecutive patients (33 eyes) diagnosed with TB posterior uveitis in a tertiary care center who underwent UWF fundus photography and fluorescein angiography (FA) between July 2014 and March 2015 were included. Complete clinical and imaging records of the patients were retrospectively reviewed. A circle simulating the central 75-degree field was drawn on UWF pseudocolor and fluorescein angiography images. Findings within the circle were compared with the information yielded by the complete image and its impact on patient management was noted.Results: The clinical manifestations of posterior tubercular uveitis included retinal vasculitis (17 eyes), multifocal serpiginoid choroiditis (13 eyes), choroidal granulomas (2 eyes) and intermediate uveitis (1 eye). UWF imaging revealed additional capillary non-perfusion areas, neovascularization, active vasculitis, and peripheral choroiditis lesions in 30/33 eyes (90.9%), which influenced treatment decision in 15 eyes (45.5%).Conclusions: UWF imaging is useful in the detection of peripheral pathologies in tubercular posterior uveitis that may influence management decisions, such as addition of immunosuppressive therapy or scatter laser photocoagulation.