Comparisons of Effective Fields of Two Ultra-Widefield Ophthalmoscopes, Optos 200Tx and Clarus 500

Deep learning system for screening AIDS-related cytomegalovirus retinitis with ultra-wide-field fundus images

Background

Ophthalmological screening for cytomegalovirus retinitis (CMVR) for HIV/AIDS patients is important to prevent lifelong blindness. Previous studies have shown good properties of automated CMVR screening using digital fundus images. However, the application of a deep learning (DL) system to CMVR with ultra-wide-field (UWF) fundus images has not been studied, and the feasibility and efficiency of this method are uncertain.

Methods

In this study, we developed, internally validated, externally validated, and prospectively validated a DL system to detect AIDS-related from UWF fundus images from different clinical datasets. We independently used the InceptionResnetV2 network to develop and internally validate a DL system for identifying active CMVR, inactive CMVR, and non-CMVR in 6960 UWF fundus images from 862 AIDS patients and validated the system in a prospective and an external validation data set using the area under the curve (AUC), accuracy, sensitivity, and specificity. A heat map identified the most important area (lesions) used by the DL system for differentiating CMVR.

Results

The DL system showed AUCs of 0.945 (95 % confidence interval [CI]: 0.929, 0.962), 0.964 (95 % CI: 0.870, 0.999) and 0.968 (95 % CI: 0.860, 1.000) for detecting active CMVR from non-CMVR and 0.923 (95 % CI: 0.908, 0.938), 0.902 (0.857, 0.948) and 0.884 (0.851, 0.917) for detecting active CMVR from non-CMVR in the internal cross-validation, external validation, and prospective validation, respectively. Deep learning performed promisingly in screening CMVR. It also showed the ability to differentiate active CMVR from non-CMVR and inactive CMVR as well as to identify active CMVR and inactive CMVR from non-CMVR (all AUCs in the three independent data sets >0.900). The heat maps successfully highlighted lesion locations.

Conclusions

Our UWF fundus image-based DL system showed reliable performance for screening AIDS-related CMVR showing its potential for screening CMVR in HIV/AIDS patients, especially in the absence of ophthalmic resources.

Source-free active domain adaptation for diabetic retinopathy grading based on ultra-wide-field fundus images

Domain adaptation (DA) is commonly employed in diabetic retinopathy (DR) grading using unannotated fundus images, allowing knowledge transfer from labeled color fundus images. Existing DAs often struggle with domain disparities, hindering DR grading performance compared to clinical diagnosis. A source-free active domain adaptation method (SFADA), which generates features of color fundus images by noise, selects valuable ultra-wide-field (UWF) fundus images through local representation matching, and adapts models using DR lesion prototypes, is proposed to upgrade DR diagnostic accuracy. Importantly, SFADA enhances data security and patient privacy by excluding source domain data. It reduces image resolution and boosts model training speed by modeling DR grade relationships directly. Experiments show SFADA significantly improves DR grading performance, increasing accuracy by 20.90% and quadratic weighted kappa by 18.63% over baseline, reaching 85.36% and 92.38%, respectively. This suggests SFADA's promise for real clinical applications.

Comparison of Standard 7-Field, Clarus, and Optos Ultrawidefield Imaging Systems for Diabetic Retinopathy (COCO Study)

Purpose

The purpose of this study was to compare diabetic retinopathy (DR) severity levels assessed from 7 standard-field stereoscopic color photographs on a 35° fundus camera to both Clarus and Optos ultrawidefield color images.

Design

Cross-sectional, comparative imaging study.

Participants

Participants with DR imaged at a single-center retina practice.

Methods

Participants were imaged on 3 cameras at a single visit with the Topcon 35° fundus camera, Clarus, and Optos. The DR Severity Scale (DRSS) level was determined within the 7-field (7F) area of each image set using the ETDRS scale. An additional global DRSS was assigned for both Clarus and Optos images using the entire visible retina. Weighted kappa (wκ) measured the agreement between cameras.

Main Outcome Measures

The primary outcome was a 3-way comparison of DRSS level within the 7F area imaged on the 3 cameras. Secondary outcomes included a comparison of the DRSS obtained with standard 7F imaging to the global DRSS of Clarus and Optos and a comparison of the global DRSS between Clarus and Optos only.

Results

Ninety-seven eyes (50 participants) were evaluated. Agreement within 1-step of ETDRS levels between standard 7F imaging and Clarus 7F was 90.1% (wκ = 0.65), and with Optos 7F in 85.9%, (wκ = 0.58). Agreement within 1-step between standard 7F imaging and Clarus global was 88.9% of eyes (wκ = 0.63), and Optos global was 85.7%, (wκ = 0.54). Agreement between Clarus and Optos global DR level within 1-step was 89.1% (wκ = 0.68). Intergrader agreement for the 7F ETDRS level was 96% for standard 7F imaging, 98% for Clarus, and 95.5% for Optos.

Conclusions

These findings suggest that when evaluating the 7F area on Clarus and Optos, DR severity grades are comparable to standard 7F imaging. However, it is important to understand the unique attributes and differences of each fundus camera when changing the type of system used in a clinical setting due to upgrading equipment. Additionally, if the facility has access to > 1 device, there should not be an exchange between cameras for the same patient.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Joint conditional generative adversarial networks for eyelash artifact removal in ultra-wide-field fundus images

Background: Ultra-Wide-Field (UWF) fundus imaging is an essential diagnostic tool for identifying ophthalmologic diseases, as it captures detailed retinal structures within a wider field of view (FOV). However, the presence of eyelashes along the edge of the eyelids can cast shadows and obscure the view of fundus imaging, which hinders reliable interpretation and subsequent screening of fundus diseases. Despite its limitations, there are currently no effective methods or datasets available for removing eyelash artifacts from UWF fundus images. This research aims to develop an effective approach for eyelash artifact removal and thus improve the visual quality of UWF fundus images for accurate analysis and diagnosis. Methods: To address this issue, we first constructed two UWF fundus datasets: the paired synthetic eyelashes (PSE) dataset and the unpaired real eyelashes (uPRE) dataset. Then we proposed a deep learning architecture called Joint Conditional Generative Adversarial Networks (JcGAN) to remove eyelash artifacts from UWF fundus images. JcGAN employs a shared generator with two discriminators for joint learning of both real and synthetic eyelash artifacts. Furthermore, we designed a background refinement module that refines background information and is trained with the generator in an end-to-end manner. Results: Experimental results on both PSE and uPRE datasets demonstrate the superiority of the proposed JcGAN over several state-of-the-art deep learning approaches. Compared with the best existing method, JcGAN improves PSNR and SSIM by 4.82% and 0.23%, respectively. In addition, we also verified that eyelash artifact removal via JcGAN could significantly improve vessel segmentation performance in UWF fundus images. Assessment via vessel segmentation illustrates that the sensitivity, Dice coefficient and area under curve (AUC) of ResU-Net have respectively increased by 3.64%, 1.54%, and 1.43% after eyelash artifact removal using JcGAN. Conclusion: The proposed JcGAN effectively removes eyelash artifacts in UWF images, resulting in improved visibility of retinal vessels. Our method can facilitate better processing and analysis of retinal vessels and has the potential to improve diagnostic outcomes.

Diabetic Macular Oedema Guidelines: An Australian Perspective

The number of people living with diabetes is expected to rise to 578 million by 2030 and to 700 million by 2045, exacting a severe socioeconomic burden on healthcare systems around the globe. This is also reflected in the increasing numbers of people with ocular complications of diabetes (namely, diabetic macular oedema (DMO) and diabetic retinopathy (DR)). In one study examining the global prevalence of DR, 35% of people with diabetes had some form of DR, 7% had PDR, 7% had DMO, and 10% were affected by these vision-threatening stages. In many regions of the world (Australia included), DR is one of the top three leading causes of vision loss amongst working age adults (20-74 years). In the management of DMO, the landmark ETDRS study demonstrated that moderate visual loss, defined as doubling of the visual angle, can be reduced by 50% or more by focal/grid laser photocoagulation. However, over the last 20 years, antivascular endothelial growth factor (VEGF) and corticosteroid therapies have emerged as alternative options for the management of DMO and provided patients with choices that have higher chances of improving vision than laser alone. In Australia, since the 2008 NHMRC guidelines, there have been significant developments in both the treatment options and treatment schedules for DMO. This working group was therefore assembled to review and address the current management options available in Australia.

Comparison of peripheral extension, acquisition time, and image chromaticity of Optos, Clarus, and EIDON systems

PURPOSE

To evaluate differences in acquisition time, peripheral extension, and chromaticity between 3 different commercialized ultra-wide-field (UWF) fundus cameras.

METHODS

Patients were prospectively enrolled from 07/2021 to 11/2021. Patients underwent fundus photography with the following scanning protocols: (1) single shot with Silverstone (Optos, California), two-shot montage with Clarus 500 (Carl Zeiss, Dublin, CA), and three-shot montage with iCare EIDON FA with UWF module (CenterVue Spa, a company of iCare Finland Oy; Vantaa, Finland). Acquisition time was calculated as the interval between the beginning and the end of the acquisition. Peripheral extension was quantified as the average ratio between the total retinal pixel area and the optic nerve head (ONH) pixel area. The average chromaticity of all pixels in the red-green-blue (RGB) space was calculated.

RESULTS

Twenty-three eyes of 13 prospectively enrolled healthy controls were included in the study. Optos Silverstone had a higher total retina area/ONH area ratio (509.1 [480.9;559.3]) compared to Zeiss Clarus (442.0 [431.9;510.5], p = 0.02) and iCare EIDON (369.7 [345.3;387.8], p < 0.0001). Silverstone demonstrated the shortest acquisition time (median [interquartile range]: 32 [20;58.5] s) compared to Zeiss Clarus (42 [28.5;53.5] s, p = 0.6733) and iCare EIDON (72 [68.5;78] s, p = 0.0003). iCare EIDON demonstrated the lowest variability of acquisition time (9.5 s), compared to Zeiss Clarus (25 s) and Optos Silverstone (38.5 s). A statistically significant difference was found in the RGB distribution between each of the 3 devices (p < 0.001). iCare EIDON demonstrated an average barycenter position (RGB = [0.412, 0.314, 0.275]) that represented the best color balance of the image. Zeiss Clarus had a noticeable red shift at the expense of the blue and green channels (RGB = [0.515, 0.294, 0.191]). Optos Silverstone showed an absence of the blue channel (RGB = [0.621, 0.372, 0.007]) which results in a distortion of the color of the image.

CONCLUSION

Optos Silverstone and Zeiss Clarus required less time than iCare EIDON to acquire a comparable size image and captured larger areas of the retina than iCare EIDON. iCare EIDON provided more color-balanced retinal images with greater richness of color content than the other two devices.

Ultra-wide-field fundus photography compared to ophthalmoscopy in diagnosing and classifying major retinal diseases

To analyze the performance of ultra-wide-field (UWF) fundus photography compared with ophthalmoscopy in identifying and classifying retinal diseases. Patients examined for presumed major retinal disorders were consecutively enrolled. Each patient underwent indirect ophthalmoscopic evaluation, with scleral depression and/or fundus biomicroscopy, when clinically indicated, and mydriatic UWF fundus imaging by means of CLARUS 500™ fundus camera. Each eye was classified by a clinical grader and two image graders in the following groups: normal retina, diabetic retinopathy, vascular abnormalities, macular degenerations and dystrophies, retinal and choroidal tumors, peripheral degenerative lesions and retinal detachment and myopic alterations. 7024 eyes of new patients were included. The inter-grader agreement for images classification was perfect (kappa = 0.998, 95% Confidence Interval (95%CI) = 0.997-0.999), as the two methods concordance for retinal diseases diagnosis (kappa = 0.997, 95%CI = 0.996-0.999) without statistically significant difference. UWF fundus imaging might be an alternative to ophthalmoscopy, since it allows to accurately classify major retinal diseases, widening the range of disorders possibly diagnosed with teleophthalmology. Although the clinician should be aware of the possibility that a minority of the most peripheral lesions may be not entirely visualized, it might be considered a first line diagnostic modality, in the context of a full ophthalmological examination.

Comparison of two ultra-widefield color-fundus imaging devices for visualization of retinal periphery and microvascular lesions in patients with early diabetic retinopathy

Comparison of two ultra-widefield (UWF) color-fundus (CF) imaging devices in diabetic patients for visualization of retinal periphery and detection of early microvascular lesions. The total gradable areas (TGA) seen on non-mydriatic CF-images of two UWF-imaging devices (Optos Daytona P200T; Zeiss Clarus 700) were compared and differences in projected area measured. Retinal periphery outside the 7 standard fields (7SF) was divided into: F3 temporal, F4 superotemporal, F5 inferotemporal, F6 superonasal, F7 inferonasal. DR stage was evaluated in the 7SF and the TGA on images of both devices and compared using Cohens κ. 67 eyes of 67 patients (52.5 ± 15.3 years) were analysed. DR stages in the 7SF were no (n = 36 Optos, n = 35 Clarus), mild (n = 16 Optos, n = 17 Clarus), and moderate DR (n = 15). Optos depicted significantly more area in F3 (median [interquartile range]; 2.41% [1.06-4.11] vs 0% [0-0], P < 0.001) and Clarus in F7 (3.29% [0-7.69] vs 0% [0-3.27], P = 0.002). In 4 eyes DR-stage was higher using Optos due to peripheral lesions not seen on the Clarus. Interrater reliability of DR-stage on both devices was almost perfect in the 7SF (κ = 0.975) and the TGA (κ = 0.855). Reliability in detecting signs of early DR is high on both devices. Clarus allowed for better visualization of the inferonasal field, Optos of the temporal field.

Comparison of Two Ultra-Widefield Cameras With High Image Resolution and Wider View for Identifying Diabetic Retinopathy Lesions

Purpose

To compare the effectiveness of the Optos P200dTx and Zeiss Clarus 500 fundus cameras in detecting diabetic retinopathy (DR) lesions.

Methods

A cross-sectional study was conducted among 243 patients with clinically diagnosed diabetes mellitus who were referred for an eye examination from two tertiary eye care centers in Chennai, India. Patients underwent DR screening based on mydriatic fundal images acquired by both fundal cameras. Fundal images from the two separate devices for each eye were compared based on accurately identified pathological retinal lesions with respect to type and location.

Results

When studying lesions of the central retina, they were better identified by the Zeiss Clarus compared with the Optos P200dTx, with six out of eight being statistically significant (P < 0.05). However, lesions of the mid-peripheral retina and peripheral retina were better identified by the Optos P200dTx than the Zeiss Clarus, with three out of eight lesions and five out of eight lesions being statistically significant (P < 0.05), respectively. Based on the color and size of lesions, the Optos P200dTx had a higher chance (59.6%) of missing white lesions than did the Zeiss Clarus (17%) (P < 0.0001). Consequently, small- and medium-sized lesions were missed more by the Optos P200dTx (30.72% and 32.63%, respectively) than the Zeiss Clarus (22.3% and 19.30%, respectively).

Conclusions

The capability of detecting or missing a particular DR lesion among diabetics differed between the two cameras based on effective field of view, resolution, and the retinal zone being imaged.

Translational Relevance

The choice of which ultra-widefield camera to be used for screening DR can be based on the greater prevalence of central versus peripheral retinal lesions noted in the patient population seen in a clinical practice.

Artificial intelligence for improving sickle cell retinopathy diagnosis and management

Sickle cell retinopathy is often initially asymptomatic even in proliferative stages, but can progress to cause vision loss due to vitreous haemorrhages or tractional retinal detachments. Challenges with access and adherence to screening dilated fundus examinations, particularly in medically underserved areas where the burden of sickle cell disease is highest, highlight the need for novel approaches to screening for patients with vision-threatening sickle cell retinopathy. This article reviews the existing literature on and suggests future research directions for coupling artificial intelligence with multimodal retinal imaging to expand access to automated, accurate, imaging-based screening for sickle cell retinopathy. Given the variability in retinal specialist practice patterns with regards to monitoring and treatment of sickle cell retinopathy, we also discuss recent progress toward development of machine learning models that can quantitatively track disease progression over time. These artificial intelligence-based applications have great potential for informing evidence-based and resource-efficient clinical diagnosis and management of sickle cell retinopathy.

Peripheral Manifestations in Age Related Macular Degeneration: A Review of Imaging and Findings

Purpose: To review novel findings in research with ultra-widefield imaging for analysis of peripheral manifestations in macular degeneration (AMD). We introduce the evolving widefield imaging modalities while summarizing the analytical techniques used in data collection of peripheral retinal findings thus far. Our review provides a summary of advancements to date and a commentary on future direction for AMD research. Methods: This is a literature review of all significant publications focused on the relationship between AMD and the retinal periphery conducted within the last two decades. Results and Conclusion: Promising research has been undertaken to elucidate peripheral retinal manifestations in macular degeneration using novel methodology. Advancements in ultra-widefield imaging and fundus autofluorescence have allowed us to elucidate peripheral retinal pigmentary changes, drusen deposition, and much more. Novel grid overlay techniques have been introduced to aid in analyzing these changes for pattern recognition and grouping of findings. This review discusses these findings in detail, providing evidence for the pan-retinal manifestations of AMD. Inter-study discordance in analytical approach highlights a need for more systematic future study.

Deep Learning Detection of Sea Fan Neovascularization From Ultra-Widefield Color Fundus Photographs of Patients With Sickle Cell Hemoglobinopathy

Importance

Adherence to screening for vision-threatening proliferative sickle cell retinopathy is limited among patients with sickle cell hemoglobinopathy despite guidelines recommending dilated fundus examinations beginning in childhood. An automated algorithm for detecting sea fan neovascularization from ultra-widefield color fundus photographs could expand access to rapid retinal evaluations to identify patients at risk of vision loss from proliferative sickle cell retinopathy.

Objective

To develop a deep learning system for detecting sea fan neovascularization from ultra-widefield color fundus photographs from patients with sickle cell hemoglobinopathy.

Design, Setting, and Participants

In a cross-sectional study conducted at a single-institution, tertiary academic referral center, deidentified, retrospectively collected, ultra-widefield color fundus photographs from 190 adults with sickle cell hemoglobinopathy were independently graded by 2 masked retinal specialists for presence or absence of sea fan neovascularization. A third masked retinal specialist regraded images with discordant or indeterminate grades. Consensus retinal specialist reference standard grades were used to train a convolutional neural network to classify images for presence or absence of sea fan neovascularization. Participants included nondiabetic adults with sickle cell hemoglobinopathy receiving care from a Wilmer Eye Institute retinal specialist; the patients had received no previous laser or surgical treatment for sickle cell retinopathy and underwent imaging with ultra-widefield color fundus photographs between January 1, 2012, and January 30, 2019.

Interventions

Deidentified ultra-widefield color fundus photographs were retrospectively collected.

Main Outcomes and Measures

Sensitivity, specificity, and area under the receiver operating characteristic curve of the convolutional neural network for sea fan detection.

Results

A total of 1182 images from 190 patients were included. Of the 190 patients, 101 were women (53.2%), and the mean (SD) age at baseline was 36.2 (12.3) years; 119 patients (62.6%) had hemoglobin SS disease and 46 (24.2%) had hemoglobin SC disease. One hundred seventy-nine patients (94.2%) were of Black or African descent. Images with sea fan neovascularization were obtained in 57 patients (30.0%). The convolutional neural network had an area under the curve of 0.988 (95% CI, 0.969-0.999), with sensitivity of 97.4% (95% CI, 86.5%-99.9%) and specificity of 97.0% (95% CI, 93.5%-98.9%) for detecting sea fan neovascularization from ultra-widefield color fundus photographs.

Conclusions and Relevance

This study reports an automated system with high sensitivity and specificity for detecting sea fan neovascularization from ultra-widefield color fundus photographs from patients with sickle cell hemoglobinopathy, with potential applications for improving screening for vision-threatening proliferative sickle cell retinopathy.

Comparison of two ultra-widefield imaging for detecting peripheral retinal breaks requiring treatment

AIM

To compare the sensitivity of Optomap Panoramic 200 and Clarus 500 in detecting peripheral retinal breaks that required treatment.

METHODS

This prospective study enrolled consecutive patients undergoing laser for treatment-requiring peripheral retinal breaks from May 2019 to July 2019. The patients first underwent indirect ophthalmoscopy examination with scleral indentation by a retinal consultant and then ultra-widefield imaging by a single trained technician on Optomap 200 and Clarus 500 in all nine ocular gazes. The images were analysed by two independent investigators to look for the number and location of the breaks. The sensitivity of each platform was calculated as the number of treatment-requiring breaks identified by the system divided by the number of breaks identified on clinical examination.

RESULTS

Clinical examination of 49 eyes (41 patients) showed 116 treatment-requiring breaks. Overall sensitivity for identifying such breaks for Optomap and Clarus was 80.2% (n = 93) and 74.1% (n = 86) respectively (p = 0.274). The sensitivities in superior (p = 0.665), temporal (p = 0.146) and inferior (p = 0.889) quadrants were statistically similar for both the platforms. The sensitivity of Optomap was slightly higher than Clarus in emmetropic (p = 0.046) and phakic (p = 0.061) eyes, but similar in myopic (p = 0.448) and pseudophakic (p = 0.191) eyes.

CONCLUSION

The ability to detect treatment-requiring retinal breaks is similar for both Optomap and Clarus systems.