Comparison of true-colour wide-field confocal scanner imaging with standard fundus photography for diabetic retinopathy screening

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.

Comparison between Deep-Learning-Based Ultra-Wide-Field Fundus Imaging and True-Colour Confocal Scanning for Diagnosing Glaucoma

In this retrospective, comparative study, we evaluated and compared the performance of two confocal imaging modalities in detecting glaucoma based on a deep learning (DL) classifier: ultra-wide-field (UWF) fundus imaging and true-colour confocal scanning. A total of 777 eyes, including 273 normal control eyes and 504 glaucomatous eyes, were tested. A convolutional neural network was used for each true-colour confocal scan (Eidon AF™, CenterVue, Padova, Italy) and UWF fundus image (Optomap™, Optos PLC, Dunfermline, UK) to detect glaucoma. The diagnostic model was trained using 545 training and 232 test images. The presence of glaucoma was determined, and the accuracy and area under the receiver operating characteristic curve (AUC) metrics were assessed for diagnostic power comparison. DL-based UWF fundus imaging achieved an AUC of 0.904 (95% confidence interval (CI): 0.861−0.937) and accuracy of 83.62%. In contrast, DL-based true-colour confocal scanning achieved an AUC of 0.868 (95% CI: 0.824−0.912) and accuracy of 81.46%. Both DL-based confocal imaging modalities showed no significant differences in their ability to diagnose glaucoma (p = 0.135) and were comparable to the traditional optical coherence tomography parameter-based methods (all p > 0.005). Therefore, using a DL-based algorithm on true-colour confocal scanning and UWF fundus imaging, we confirmed that both confocal fundus imaging techniques had high value in diagnosing glaucoma.

Understanding the relationship between visual-angle and eye-angle for reliable determination of the field-of-view in ultra-wide field fundus photography

Visual-angle has been used as the conventional unit to determine the field-of-view (FOV) in traditional fundus photography. Recently emerging usage of eye-angle as the unit in wide field fundus photography creates confusion about FOV interpretation in instrumentation design and clinical application. This study aims to systematically derive the relationship between the visual-angle θv and eye-angle θe, and thus to enable reliable determination of the FOV in wide field fundus photography. FOV conversion ratio θe/θv, angular conversion ratio Δθe/Δθv, retinal conversion ratio Δd/Δθv, retinal distance and area are quantitatively evaluated. Systematic analysis indicates that reliable conversion between the θv and θe requires determined nodal point and spherical radius of the eye; and the conversion ratio is not linear from the central field to peripheral region. Based on the eye model with average parameters, both angular conversion (Δθe/Δθv) and retinal conversion (Δd/Δθv) ratios are observed to have a 1.51-fold difference at the central field and far peripheral region. A conversion table, including θe/θv, Δθe/Δθv, Δd/Δθv, retinal area and percentage ratio, is created for reliable assessment of imaging systems with variable FOV.

Effect of ethnicity and other sociodemographic factors on attendance at diabetic eye screening: a 12-month retrospective cohort study

OBJECTIVES

To examine the association of sociodemographic characteristics with attendance at diabetic eye screening in a large ethnically diverse urban population.

DESIGN

Retrospective cohort study.

SETTING

Screening visits in the North East London Diabetic Eye Screening Programme (NELDESP).

PARTICIPANTS

84 449 people with diabetes aged 12 years or older registered in the NELDESP and scheduled for screening between 1 April 2017 and 31 March 2018.

MAIN OUTCOME MEASURE

Attendance at diabetic eye screening appointments.

RESULTS

The mean age of people with diabetes was 60 years (SD 14.2 years), 53.4% were men, 41% South Asian, 29% White British and 17% Black; 83.4% attended screening. Black people with diabetes had similar levels of attendance compared with White British people. However, South Asian, Chinese and 'Any other Asian' background ethnicities showed greater odds of attendance compared with White British. When compared with their respective reference group, high levels of deprivation, younger age, longer duration of diabetes and worse visual acuity, were all associated with non-attendance. There was a higher likelihood of attendance per quintile improvement in deprivation (OR, 1.06; 95% CI, 1.03 to 1.08), with increasing age (OR per decade, 1.17; 95% CI, 1.15 to 1.19), with better visual acuity (OR per Bailey-Lovie chart line 1.12; 95% CI, 1.11 to 1.14) and with longer time of NELDESP registration (OR per year, 1.02; 95% CI, 1.01 to 1.03).

CONCLUSION

Ethnic differences in diabetic eye screening uptake, though small, are evident. Despite preconceptions, a higher likelihood of screening attendance was observed among Asian ethnic groups when compared with the White ethnic group. Poorer socioeconomic profile was associated with higher likelihood of non-attendance for screening. Further work is needed to understand how to target individuals at risk of non-attendance and reduce inequalities.

Recent Advances and Clinical Application of Color Scanning Laser Ophthalmoscope

Scanning laser ophthalmoscopes (SLOs) have been available since the early 1990s, but they were not commonly used because their advantages were not enough to replace conventional color fundus photography. In recent years, color SLOs have improved significantly, and the colored SLO images are obtained by combining multiple SLO images taken by lasers of different wavelengths. A combination of these images of different lasers can create an image that is close to that of the real ocular fundus. One advantage of the advanced SLOs is that they can obtain images with a wider view of the ocular fundus while maintaining a high resolution even through non-dilated eyes. The current SLOs are superior to the conventional fundus photography in their ability to image abnormal alterations of the retina and choroid. Thus, the purpose of this review was to present the characteristics of the current color SLOs and to show how that can help in the diagnosis and the following of changes after treatments. To accomplish these goals, we will present our findings in patients with different types of retinochoroidal disorders.

The Evolution of Diabetic Retinopathy Screening Programmes: A Chronology of Retinal Photography from 35 mm Slides to Artificial Intelligence

As a third of people with diabetes mellitus (DM) will suffer the microvascular complications of diabetic retinopathy (DR) and therapeutic options can effectively prevent visual impairment, systematic screening has substantially reduced disease burden in developed countries. In an effort to tackle the rising incidence of DM, screening programmes have modernized in synchrony with technical and infrastructural advancements. Patient evaluation has shifted from face-to-face ophthalmologist-based review delivered through community grassroots to asynchronous store-and-forward modern telemedicine platforms commissioned on a nationwide scale. First pioneered with primitive 35-mm slide film retinal photography, the last decade has seen an emergence of high resolution and widefield imaging devices, which may reveal extents of DR indiscernible to the clinician but with implications of potential earlier identification. Similar progress has been seen in image analysis approaches - automated image analysis of retinal photographs of DR has evolved from qualitative feature detection to rules-based algorithms to autonomous artificial intelligence-powered classification. Such models have, relatively rapidly, been validated and are now receiving approval from health regulation authorities with deployment into the clinical sphere. In this review, we chart the evolution of global DR screening programmes since their inception highlighting major milestones in healthcare infrastructure, telemedicine approaches and imaging devices that have shaped the robust and effective frameworks recognised today. We also provide an outlook for the future of DR screening in the context of recent technological advancements with respect to their limitations in current times.

Diagnostic accuracy of diabetic retinopathy grading by an artificial intelligence-enabled algorithm compared with a human standard for wide-field true-colour confocal scanning and standard digital retinal images

BACKGROUND

Photographic diabetic retinopathy screening requires labour-intensive grading of retinal images by humans. Automated retinal image analysis software (ARIAS) could provide an alternative to human grading. We compare the performance of an ARIAS using true-colour, wide-field confocal scanning images and standard fundus images in the English National Diabetic Eye Screening Programme (NDESP) against human grading.

METHODS

Cross-sectional study with consecutive recruitment of patients attending annual diabetic eye screening. Imaging with mydriasis was performed (two-field protocol) with the EIDON platform (CenterVue, Padua, Italy) and standard NDESP cameras. Human grading was carried out according to NDESP protocol. Images were processed by EyeArt V.2.1.0 (Eyenuk Inc, Woodland Hills, California). The reference standard for analysis was the human grade of standard NDESP images.

RESULTS

We included 1257 patients. Sensitivity estimates for retinopathy grades were: EIDON images; 92.27% (95% CI: 88.43% to 94.69%) for any retinopathy, 99% (95% CI: 95.35% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. For NDESP images: 92.26% (95% CI: 88.37% to 94.69%) for any retinopathy, 100% (95% CI: 99.53% to 100%) for vision-threatening retinopathy and 100% (95% CI: 61% to 100%) for proliferative retinopathy. One case of vision-threatening retinopathy (R1M1) was missed by the EyeArt when analysing the EIDON images, but identified by the human graders. The EyeArt identified all cases of vision-threatening retinopathy in the standard images.

CONCLUSION

EyeArt identified diabetic retinopathy in EIDON images with similar sensitivity to standard images in a large-scale screening programme, exceeding the sensitivity threshold recommended for a screening test. Further work to optimise the identification of 'no retinopathy' and to understand the differential lesion detection in the two imaging systems would enhance the use of these two innovative technologies in a diabetic retinopathy screening setting.