Dataset Information

Validation of handheld fundus camera with mydriasis for retinal imaging of diabetic retinopathy screening in China: a prospective comparison study.

ABSTRACT: OBJECTIVES:To investigate the clinical validity of using a handheld fundus camera to detect diabetic retinopathy (DR) in China. DESIGN AND SETTINGS:Prospective comparison study of the handheld fundus camera with a standard validated instrument in detection of DR in hospital and a community screening clinic in Guangdong Province, China. PARTICIPANTS:Participants aged 18 years and over with diabetes who were able to provide informed consent and agreed to attend the dilated eye examination with handheld tests and a standard desktop camera. PRIMARY AND SECONDARY OUTCOME MEASURES:Primary outcome was the proportion of those with referable DR (R2 and above) identified by the handheld fundus camera (the index test) compared with the standard camera. Secondary outcome was the comparison of proportion of gradable images obtained from each test. RESULTS:In this study, we examined 304 people (608 eyes) with each of the two cameras under mydriasis. The handheld camera detected 119 eyes (19.5%) with some level of DR, 81 (13.3%) of them were referable, while the standard camera detected 132 eyes (21.7%) with some level of DR and 83 (13.7%) were referable. It seems that the standard camera found more eyes with referable DR, although McNemar's test detected no significant difference between the two cameras.Of the 608 eyes with images obtained by desktop camera, 598 (98.4%) images were of sufficient quality for grading, 12 (1.9%) images were not gradable. By the handheld camera, 590 (97.0%) were gradable and 20 (3.2%) images were not gradable.The two cameras reached high agreement on diagnosis of retinopathy and maculopathy at all the levels of retinopathy. CONCLUSION:Although it could not take the place of standard desktop camera on clinic fundus examination, the handheld fundus camera showed promising role on preliminary DR screening at primary level in China. To ensure quality images, mydriasis is required.

SUBMITTER: Xiao B

PROVIDER: S-EPMC7597494 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Validation of handheld fundus camera with mydriasis for retinal imaging of diabetic retinopathy screening in China: a prospective comparison study.

Xiao Baixiang B Liao Qinghua Q Li Yanping Y Weng Fan F Jin Ling L Wang Yanfang Y Huang Wenyong W Yi Jinglin J Burton Matthew J MJ Yip Jennifer Ly JL

BMJ open 20201029 10

<h4>Objectives</h4>To investigate the clinical validity of using a handheld fundus camera to detect diabetic retinopathy (DR) in China.<h4>Design and settings</h4>Prospective comparison study of the handheld fundus camera with a standard validated instrument in detection of DR in hospital and a community screening clinic in Guangdong Province, China.<h4>Participants</h4>Participants aged 18 years and over with diabetes who were able to provide informed consent and agreed to attend the dilated ey ...[more]

PMID: 33122324

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Project description:BackgroundDiabetic retinopathy (DR) is a leading cause of blindness. Our objective was to evaluate the performance of an artificial intelligence (AI) system integrated into a handheld smartphone-based retinal camera for DR screening using a single retinal image per eye.MethodsImages were obtained from individuals with diabetes during a mass screening program for DR in Blumenau, Southern Brazil, conducted by trained operators. Automatic analysis was conducted using an AI system (EyerMaps™, Phelcom Technologies LLC, Boston, USA) with one macula-centered, 45-degree field of view retinal image per eye. The results were compared to the assessment by a retinal specialist, considered as the ground truth, using two images per eye. Patients with ungradable images were excluded from the analysis.ResultsA total of 686 individuals (average age 59.2 ± 13.3 years, 56.7% women, diabetes duration 12.1 ± 9.4 years) were included in the analysis. The rates of insulin use, daily glycemic monitoring, and systemic hypertension treatment were 68.4%, 70.2%, and 70.2%, respectively. Although 97.3% of patients were aware of the risk of blindness associated with diabetes, more than half of them underwent their first retinal examination during the event. The majority (82.5%) relied exclusively on the public health system. Approximately 43.4% of individuals were either illiterate or had not completed elementary school. DR classification based on the ground truth was as follows: absent or nonproliferative mild DR 86.9%, more than mild (mtm) DR 13.1%. The AI system achieved sensitivity, specificity, positive predictive value, and negative predictive value percentages (95% CI) for mtmDR as follows: 93.6% (87.8-97.2), 71.7% (67.8-75.4), 42.7% (39.3-46.2), and 98.0% (96.2-98.9), respectively. The area under the ROC curve was 86.4%.ConclusionThe portable retinal camera combined with AI demonstrated high sensitivity for DR screening using only one image per eye, offering a simpler protocol compared to the traditional approach of two images per eye. Simplifying the DR screening process could enhance adherence rates and overall program coverage.

Project description:PurposeTo assess cone density as a marker of early signs of retinopathy in patients with type II diabetes mellitus.MethodsAn adaptive optics (AO) retinal camera (rtx1™; Imagine Eyes, Orsay, France) was used to acquire images of parafoveal cones from patients with type II diabetes mellitus with or without retinopathy and from healthy controls with no known systemic or ocular disease. Cone mosaic was captured at 0° and 2°eccentricities along the horizontal and vertical meridians. The density of the parafoveal cones was calculated within 100×100-μm squares located at 500-μm from the foveal center along the orthogonal meridians. Manual corrections of the automated counting were then performed by 2 masked graders. Cone density measurements were evaluated with ANOVA that consisted of one between-subjects factor, stage of retinopathy and the within-subject factors. The ANOVA model included a complex covariance structure to account for correlations between the levels of the within-subject factors.ResultsTen healthy participants (20 eyes) and 25 patients (29 eyes) with type II diabetes mellitus were recruited in the study. The mean (± standard deviation [SD]) age of the healthy participants (Control group), patients with diabetes without retinopathy (No DR group), and patients with diabetic retinopathy (DR group) was 55 ± 8, 53 ± 8, and 52 ± 9 years, respectively. The cone density was significantly lower in the moderate nonproliferative diabetic retinopathy (NPDR) and severe NPDR/proliferative DR groups compared to the Control, No DR, and mild NPDR groups (P < 0.05). No correlation was found between cone density and the level of hemoglobin A1c (HbA1c) or the duration of diabetes.ConclusionsThe extent of photoreceptor loss on AO imaging may correlate positively with severity of DR in patients with type II diabetes mellitus. Photoreceptor loss may be more pronounced among patients with advanced stages of DR due to higher risk of macular edema and its sequelae.

Project description:Retinal blood vessels are considered valuable biomarkers for the detection of diabetic retinopathy, hypertensive retinopathy, and other retinal disorders. Ophthalmologists analyze retinal vasculature by manual segmentation, which is a tedious task. Numerous studies have focused on automatic retinal vasculature segmentation using different methods for ophthalmic disease analysis. However, most of these methods are computationally expensive and lack robustness. This paper proposes two new shallow deep learning architectures: dual-stream fusion network (DSF-Net) and dual-stream aggregation network (DSA-Net) to accurately detect retinal vasculature. The proposed method uses semantic segmentation in raw color fundus images for the screening of diabetic and hypertensive retinopathies. The proposed method's performance is assessed using three publicly available fundus image datasets: Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of Retina (STARE), and Children Heart Health Study in England Database (CHASE-DB1). The experimental results revealed that the proposed method provided superior segmentation performance with accuracy (Acc), sensitivity (SE), specificity (SP), and area under the curve (AUC) of 96.93%, 82.68%, 98.30%, and 98.42% for DRIVE, 97.25%, 82.22%, 98.38%, and 98.15% for CHASE-DB1, and 97.00%, 86.07%, 98.00%, and 98.65% for STARE datasets, respectively. The experimental results also show that the proposed DSA-Net provides higher SE compared to the existing approaches. It means that the proposed method detected the minor vessels and provided the least false negatives, which is extremely important for diagnosis. The proposed method provides an automatic and accurate segmentation mask that can be used to highlight the vessel pixels. This detected vasculature can be utilized to compute the ratio between the vessel and the non-vessel pixels and distinguish between diabetic and hypertensive retinopathies, and morphology can be analyzed for related retinal disorders.

Dataset Information

Validation of handheld fundus camera with mydriasis for retinal imaging of diabetic retinopathy screening in China: a prospective comparison study.

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Validation of handheld fundus camera with mydriasis for retinal imaging of diabetic retinopathy screening in China: a prospective comparison study.

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