Impact of mydriasis on image gradability and automated diabetic retinopathy screening with a handheld camera. A real-world setting evaluation.

Objectives: Sight loss due to diabetic retinopathy (DR) is preventable by early detection and treatment. Digital retinal imaging is the most widely practiced method of screening of DR. Poor quality of images is a major hinder to implement systematic DR screening using digital retinal imaging in low- and middle-income countries with a high prevalence of lens opacities. We aimed to identify the proportion of ungradable images using hand-held retinal imaging and predictors of image gradability in a DR screening feasibility study conducted in Sri Lanka. Material and Methods: The present study is a retrospective data analysis of a validation study conducted to assess the diagnostic test accuracy of a held-held digital retinal imaging model integrated into a tertiary level medical clinic. Two selected physician graders underwent formal training to assess retinal image quality using a “four-quadrant method of assessing gradability.” The procedure was a subjective image quality assessment performed by the physician graders manually, that is, images with more than 50% of the field with poor clarity and not suitable for retinopathy grading were classified as ungradable. Two-field (Field-1: macula centered, Field-2: disc centered) non-mydriatic and mydriatic retinal images were captured (Type of camera: Zeiss-Visuscout 100®, Germany) in a consecutive sample of people with diabetes attended for routine medical care and assessed for quality and graded by two independent physician graders on-site. The reference test was a mydriatic biomicroscopic examination conducted at a separate eye clinic by an experienced specialist retinologist. Mixed model regression analysis was conducted to assess the predictors of gradability. Results: A total of 700 individuals (5508 gradability data points) were included in the study. The proportion of ungradable images in non-mydriatic imaging was 30% for Grader 1 and 24% for Grader 2 and went down to 7% and 5%, respectively, for Grader 1 and 2 after dilating the pupils using mydriatic agents. Non-mydriatic images had almost 20 times higher odds (odds ratio [OR] 19.7, 95% confidence interval [CI] 15.1–25.8) of being rated as ungradable compared to mydriatic images. With the increase of each year in age, the odds of having ungradable digital retinal images in a patient increased by 7% (OR 1.07, 95% CI 1.05–1.09). For visual acuity increase in each level of the Log-MAR scale, the odds of having ungradable images increased by 40% (OR 1.40, 95% CI 1.30–1.51). In lens opacity, 54% higher odds of ungradability were observed when present nuclear opalescence (OR 1.54, 95% CI 1.39–1.70) and posterior subcapsular opacity (OR 1.54, 95% CI 1.24–1.92). Conclusion: Non-mydriatic methods may not be suitable as a primary DR screening strategy in countries with a high prevalence of cataracts. Increasing age, poor visual acuity, and the presence of lens opacity are factors that would affect image quality. The capacity to deliver services for managing cataracts may be an important determinant in achieving effective coverage of digital surveillance of DR in low- and middle-income countries.

AimsThis study aims to compare the performance of a handheld fundus camera (Eyer) and standard tabletop fundus cameras (Visucam 500, Visucam 540, and Canon CR-2) for diabetic retinopathy and diabetic macular edema screening.MethodsThis was a multicenter, cross-sectional study that included images from 327 individuals with diabetes. The participants underwent pharmacological mydriasis and fundus photography in two fields (macula and optic disk centered) with both strategies. All images were acquired by trained healthcare professionals, de-identified, and graded independently by two masked ophthalmologists, with a third senior ophthalmologist adjudicating in discordant cases. The International Classification of Diabetic Retinopathy was used for grading, and demographic data, diabetic retinopathy classification, artifacts, and image quality were compared between devices. The tabletop senior ophthalmologist adjudication label was used as the ground truth for comparative analysis. A univariate and stepwise multivariate logistic regression was performed to determine the relationship of each independent factor in referable diabetic retinopathy.ResultsThe mean age of participants was 57.03 years (SD 16.82, 9–90 years), and the mean duration of diabetes was 16.35 years (SD 9.69, 1–60 years). Age (P = .005), diabetes duration (P = .004), body mass index (P = .005), and hypertension (P < .001) were statistically different between referable and non-referable patients. Multivariate logistic regression analysis revealed a positive association between male sex (OR 1.687) and hypertension (OR 3.603) with referable diabetic retinopathy. The agreement between devices for diabetic retinopathy classification was 73.18%, with a weighted kappa of 0.808 (almost perfect). The agreement for macular edema was 88.48%, with a kappa of 0.809 (almost perfect). For referable diabetic retinopathy, the agreement was 85.88%, with a kappa of 0.716 (substantial), sensitivity of 0.906, and specificity of 0.808. As for image quality, 84.02% of tabletop fundus camera images were gradable and 85.31% of the Eyer images were gradable.ConclusionsOur study shows that the handheld retinal camera Eyer performed comparably to standard tabletop fundus cameras for diabetic retinopathy and macular edema screening. The high agreement with tabletop devices, portability, and low costs makes the handheld retinal camera a promising tool for increasing coverage of diabetic retinopathy screening programs, particularly in low-income countries. Early diagnosis and treatment have the potential to prevent avoidable blindness, and the present validation study brings evidence that supports its contribution to diabetic retinopathy early diagnosis and treatment.

Purpose: To analyze predictors of image quality for a handheld nonmydriatic fundus camera used for screening of vision-threatening diabetic retinopathy. Methods: An ophthalmic photographer at an Aravind Eye Hospital obtained nonmydriatic and mydriatic fundus images from 3 fields in 275 eyes of 155 participants over 13 months using a Smartscope camera (Optomed, Oulu, Finland) and a Topcon tabletop fundus camera (Topcon, Tokyo, Japan). Two fellowship-trained retina specialists graded the images. Repeated-measures logistic regression assessed predictors of the main outcome measure: gradability of the fundus images. Results: Of 2,475 images, 76.2% of the Smartscope nonmydriatic images, 90.1% of the Smartscope mydriatic images, and 92.0% of the Topcon mydriatic images were gradable. Eyes with vitreous hemorrhage (OR = 0.24, p < 0.0001) or advanced cataract (OR = 0.08, p < 0.0001) had decreased odds of image gradability. Excluding eyes with cataract or vitreous hemorrhage, nonmydriatic macular image gradability improved from 68.4% in the first set of 55 eyes to 94.6% in the final set of 55 eyes. Conclusion: With sufficient training, paraprofessional health care staff can obtain high-quality images with a portable nonmydriatic fundus camera, particularly in patients with clear lenses and clear ocular media.

Static vessel analysis is a method to determine the diameter of retinal vessels in images of the ocular fundus. The suitability of non-mydriatic and mydriatic images for that method and the influence of mydriasis on the results were examined. In the prospective study, 30 eyes of 15 patients (10 women, mean age 51.6 +/- 13.2 years) were examined. At first, 3 images were taken of each eye with the retinal camera Topcon NW 200 (magnification 1). After pupil dilation with tropicamid eye drops, 3 more images were taken using the Topcon and 3 others using the system Visualis (IMEDOS, Jena/Germany, FF450plus, 535-561 nm, 30 degrees image, 1840 x 1360 pixel). The vessel diameters were measured with the software Vesselmap2 (IMEDOS). The investigator assigned vessels to arteries or veins and their diameters were calculated automatically by the software. There is the possibility to define the vessel edge manually in cases of a poor image quality. The calculation of the central retinal arterial and venous equivalent (CRAE, CRVE) as well as the arterio-venous ratio (AVR) were made according to the formula of Parr-Hubbard. Furthermore, the nasal retinal vessels > 60 microm were examined to estimate the influence of tropicamid on the vessel diameter. Because of insufficient illumination and poor contrast in 21 % of the non-mydriatic images not all vessels could be detected automatically. Additionally, fewer vessels could be detected in 7 % of the non-mydriatic images compared to the mydriatic images. The average coefficient of variation of CRAE and AVR of each triplet of images was higher in non-mydriatic images (2.6 % and 3.2 %, respectively) than in mydriatic images of the Topcon (1.8 %; 2.3 %) and the FF 450 (1.7 %; 1.8 % ANOVA p < 0.05). No significant differences were found between the various examination methods for both the coefficient of variation of CRVE (1.9 %; 1.8 %; 1.7 %) and the average values of CRAE, CRVE and AVR. With regard to their diameters, the nasal retinal arteries and veins > 60 microm, were depicted sufficiently in all images, and only differed insignificantly between the three methods. The quality of non-mydriatic images is often lower than that of mydriatic images. This fact can account for the high variance of measured parameters in the non-mydriatic images. The depiction of all relevant vessel segments is a precondition for the image-based analysis. An influence of the mydriasis caused by tropicamid on the retinal vessel diameters > 60 microm was not found.

BackgroundThe evidence on diagnostic test accuracy (DTA) of diabetic retinopathy (DR) screening utilising photographic studies by non-ophthalmologist personnel in low and middle-income country (LMIC) settings is scarce. We aimed to assess DTA of DR screening using a nonmydriatic hand-held digital camera by trained general physicians in a non-ophthalmic setting.MethodsThis study is a validation of a screening intervention. We selected 700 people with diabetes (PwDM) > 18 years of age, not previously screened or treated for DR, presenting at a tertiary medical clinic in Sri Lanka. Two-field retinal imaging was used to capture fundus images before and after pupil dilatation, using a hand-held non-mydriatic (Visuscout 100®-Germany) digital retinal camera. The images were captured and graded by two trained, masked independent physician graders. The DTA of different levels of DR was assessed comparing physician’s grading with a retinologist’s clinical examination by mydriatic bio-microscopy, according to a locally adopted guideline.ResultsSeven hundred eligible PwDM were screened by physician graders. The mean age of participants was 60.8 years (SD ±10.08) and mean duration of DM was 9.9 years (SD ±8.09). Ungradable image proportion in non-mydriatic imaging was 43.4% (either eye-31.3%, both eyes 12.1%). This decreased to 12.8% (either eye-11.6%, both eyes-1.2%) following pupil dilatation. In comparison to detection of any level of DR, a referable level DR (moderate non-proliferative DR and levels above) showed a higher level of DTA. The sensitivity of the defined referable DR was 88.7% (95% CI 81.7–93.8%) for grader 1 (positive predictive value [PPV] 59.1%) and 92.5% (95% CI 86.4–96.5%) for grader 2 (PPV 68%), using mydriatic imaging, after including ungradable images as screen positives. The specificity was 94.9% (95% CI 93.6–96.0%) for grader 1 (negative predictive value [NPV] 99%) and 96.4% (95% CI 95.3–97.3%) for grader 2 (NPV 99.4%).ConclusionsThe Physicians grading of images from a digital hand-held non-mydriatic camera at a medical clinic, with dilatation of pupil of those who have ungradable images, provides a valid modality to identify referable level of DR. This could be a feasible alternative modality to the existing opportunistic screening to improve the access and coverage.Trial registrationCurrent Controlled Trials ISRCTN47559703. Date of Registration 18th March 2019, Retrospectively registered.

Digital health in medicine: Important considerations in evaluating health economic analysis

Our study aimed to assess the role of a hand-held fundus camera and artificial intelligence (AI)-based grading system in diabetic retinopathy (DR) screening and determine its diagnostic accuracy in detecting DR compared with clinical examination and a standard fundus camera. This cross-sectional instrument validation study, as a part of the International Diabetes Federation (IDF) Diabetic Retinopathy Screening Project, included 160 patients (320 eyes) with type 2 diabetes (T2DM). After the standard indirect slit-lamp fundoscopy, each patient first underwent fundus photography with a standard 45° camera VISUCAM Zeiss and then with a hand-held camera TANG (Shanghai Zhi Tang Health Technology Co., Ltd.). Two retina specialists independently graded the images taken with the standard camera, while the images taken with the hand-held camera were graded using the DeepDR system and an independent IDF ophthalmologist. The three screening methods did not differ in detecting moderate/severe nonproliferative and proliferative DR. The area under the curve, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, kappa (ĸ) agreement, diagnostic odds ratio, and diagnostic effectiveness for a hand-held camera compared to clinical examination were 0.921, 89.1%, 100%, 100%, 91.4%, infinity, 0.11, 0.86, 936.48, and 94.9%, while compared to the standard fundus camera were 0.883, 83.2%, 100%, 100%, 87.3%, infinity, 0.17, 0.78, 574.6, and 92.2%. The results of our study suggest that fundus photography with a hand-held camera and AI-based grading system is a short, simple, and accurate method for the screening and early detection of DR, comparable to clinical examination and fundus photography with a standard camera.

To explore the performance in diabetic retinopathy (DR) screening of artificial intelligence (AI) system by evaluating the image quality of a handheld Optomed Aurora fundus camera in comparison to traditional tabletop fundus cameras and the diagnostic accuracy of DR of the two modalities. Overall, 630 eyes were included from three centers and screened by a handheld camera (Aurora, Optomed, Oulu, Finland) and a table-top camera. Image quality was graded by three masked and experienced ophthalmologists. The diagnostic accuracy of the handheld camera and AI system was evaluated in assessing DR lesions and referable DR. Under nonmydriasis status, the handheld fundus camera had better image quality in centration, clarity, and visible range (1.47, 1.48, and 1.40) than conventional tabletop cameras (1.30, 1.28, and 1.18; P<0.001). Detection of retinal hemorrhage, hard exudation, and macular edema were comparable between the two modalities, in principle, with the area under the curve of the handheld fundus camera slightly lower. The sensitivity and specificity for the detection of referable DR with the handheld camera were 82.1% (95%CI: 72.1%-92.2%) and 97.4% (95%CI: 95.4%-99.5%), respectively. The performance of AI detection of DR using the Phoebus Algorithm was satisfactory; however, Phoebus showed a high sensitivity (88.2%, 95%CI: 79.4%-97.1%) and low specificity (40.7%, 95%CI: 34.1%-47.2%) when detecting referable DR. The handheld Aurora fundus camera combined with autonomous AI system is well-suited in DR screening without mydriasis because of its high sensitivity of DR detection as well as its image quality, but its specificity needs to be improved with better modeling of the data. Use of this new system is safe and effective in the detection of referable DR in real world practice.

BackgroundDiabetic retinopathy can cause blindness even in the absence of symptoms. Although routine eye screening remains the mainstay of diabetic retinopathy treatment and it can prevent 95% of blindness, this screening is not available in many low- and middle-income countries even though these countries contribute to 75% of the global diabetic retinopathy burden.ObjectiveThe aim of this study was to assess the diagnostic accuracy of diabetic retinopathy screening done by non-ophthalmologists using 2 different digital fundus cameras and to assess the risk factors for the occurrence of diabetic retinopathy.MethodsThis validation study was conducted in 6 peripheral health facilities in Bangladesh from July 2017 to June 2018. A double-blinded diagnostic approach was used to test the accuracy of the diabetic retinopathy screening done by non-ophthalmologists against the gold standard diagnosis by ophthalmology-trained eye consultants. Retinal images were taken by using either a desk-based camera or a hand-held camera following pupil dilatation. Test accuracy was assessed using measures of sensitivity, specificity, and positive and negative predictive values. Overall agreement with the gold standard test was reported using the Cohen kappa statistic (κ) and area under the receiver operating curve (AUROC). Risk factors for diabetic retinopathy occurrence were assessed using binary logistic regression.ResultsIn 1455 patients with diabetes, the overall sensitivity to detect any form of diabetic retinopathy by non-ophthalmologists was 86.6% (483/558, 95% CI 83.5%-89.3%) and the specificity was 78.6% (705/897, 95% CI 75.8%-81.2%). The accuracy of the correct classification was excellent with a desk-based camera (AUROC 0.901, 95% CI 0.88-0.92) and fair with a hand-held camera (AUROC 0.710, 95% CI 0.67-0.74). Out of the 3 non-ophthalmologist categories, registered nurses and paramedics had strong agreement with kappa values of 0.70 and 0.85 in the diabetic retinopathy assessment, respectively, whereas the nonclinical trained staff had weak agreement (κ=0.35). The odds of having retinopathy increased with the duration of diabetes measured in 5-year intervals (P<.001); the odds of having retinopathy in patients with diabetes for 5-10 years (odds ratio [OR] 1.81, 95% CI 1.37-2.41) and more than 10 years (OR 3.88, 95% CI 2.91-5.15) were greater than that in patients with diabetes for less than 5 years. Obesity was found to have a negative association (P=.04) with diabetic retinopathy.ConclusionsDigital fundus photography is an effective screening tool with acceptable diagnostic accuracy. Our findings suggest that diabetic retinopathy screening can be accurately performed by health care personnel other than eye consultants. People with more than 5 years of diabetes should receive priority in any community-level retinopathy screening program. In a country like Bangladesh where no diabetic retinopathy screening services exist, the use of hand-held cameras can be considered as a cost-effective option for potential system-wide implementation.

Screening effectively identifies patients at risk of sight-threatening diabetic retinopathy (STDR) when retinal images are captured through dilated pupils. Pharmacological mydriasis is not logistically feasible in non-clinical, community DR screening, where acquiring gradable retinal images using handheld devices exhibits high technical failure rates, reducing STDR detection. Deep learning (DL) based gradability predictions at acquisition could prompt device operators to recapture insufficient quality images, increasing gradable image proportions and consequently STDR detection. Non-mydriatic retinal images were captured as part of SMART India, a cross-sectional, multi-site, community-based, house-to-house DR screening study between August 2018 and December 2019 using the Zeiss Visuscout 100 handheld camera. From 18,277 patient eyes (40,126 images), 16,170 patient eyes (35,319 images) were eligible and 3261 retinal images (1490 patient eyes) were sampled then labelled by two ophthalmologists. Compact DL model area under the receiver operator characteristic curve was 0.93 (0.01) following five-fold cross-validation. Compact DL model agreement (Kappa) were 0.58, 0.69 and 0.69 for high specificity, balanced sensitivity/specificity and high sensitivity operating points compared to an inter-grader agreement of 0.59. Compact DL gradability model performance was favourable compared to ophthalmologists. Compact DL models can effectively classify non-mydriatic, handheld retinal image gradability with potential applications within community-based DR screening.

This paper presents a system that can automatically determine whether the quality of a retinal image is sufficient for computer-based diabetic retinopathy (DR) screening. The system integrates global histogram features, textural features, and vessel density, as well as a local non-reference perceptual sharpness metric. A partial least square (PLS) classifier is trained to distinguish low quality images from normal quality images. The system was evaluated on a large, representative set of 1884 non-mydriatic retinal images from 412 subjects. An area under the ROC curve of 96% was achieved.

Diabetic Retinopathy Screening Using Smartphone-Based Fundus Imaging in India

Diabetic Retinopathy Telemedicine Outcomes With Artificial Intelligence-Based Image Analysis, Reflex Dilation, and Image Overread

Background: Diabetic retinopathy (DR) causes blindness of the population in many countries worldwide. Early detection and treatment of this disease via a DR screening program is the best way to secure the vision. An annual screening program using pharmacological pupil dilatation becomes the standard method. Recently, non-mydriatic ultrawide-field fundus photography (UWF) has been proposed as a choice for DR screening. However, there was no cost-effectiveness study between the standard DR screening and this UWF approach. Objective: To compare the cost-effectiveness between UWF and pharmacological pupil dilatation in terms of hospital and societal perspectives. Materials and Methods: Patients with type 2 diabetes mellitus that visited the ophthalmology clinic at Chulabhorn Hospital for DR screening were randomized using simple randomization method. The patients were interviewed by a trained interviewer for general and economic information. The clinical characteristics of DR and staging were recorded. Direct medical costs, direct non-medical costs, and informal care costs due to DR screening were recorded. Cost analyses were calculated for the hospital and societal perspectives. Results: The present study presented the cost-effectiveness analyses of UWF versus pharmacological pupil dilatation. Cost-effectiveness analysis from the hospital perspective showed the incremental cost-effectiveness ratio (ICER) of UWF to be –13.87. UWF was a cost-effective mean in DR screening in the societal perspective when compared with pharmacologically pupil dilatation with the ICER of 76.46, under the threshold of willingness to pay. Conclusion: The UWF was a cost-effective mean in DR screening. It can reduce screening duration and bypass post-screening blurred vision. The results suggested that UWF could be a viable option for DR screening. Keywords: Diabetic retinopathy, Diabetic retinopathy screening, Non-mydriatic ultrawide-field fundus photography, Cost-effectiveness analysis

Diagnostic Accuracy of Artificial Intelligence-Based Automated Diabetic Retinopathy Screening in Real-World Settings: A Systematic Review and Meta-Analysis