ArticleIndian journal of ophthalmology2018
Analysis of yield of retinal imaging in a rural diabetes eye care model.
Article in Indian journal of ophthalmology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 22 citations in OpenAlex.
- Using artificial intelligence-assisted retinal imaging devices operated by non-specialist workers in detecting diabetic retinopathy in Sri Lanka.BMC global and public health · 2026Article
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- Management of diabetic ocular complications: from cellular insights to community strategies.BMC ophthalmology · 2024Article
- Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera.Journal of diabetes science and technology · 2022Article
- Impact of Implementing Teleophthalmology Referral Guidelines Using the eyeSmart EMR App in 63,703 Patients from India.International journal of telemedicine and applications · 2022Article
- Various models for diabetic retinopathy screening that can be applied to India.Indian journal of ophthalmology · 2021Review
- Training of nonophthalmologists in diabetic retinopathy screening.Indian journal of ophthalmology · 2021Article
- Article
- Capacity building for diabetic retinopathy screening by optometrists in India: Model description and pilot results.Indian journal of ophthalmology · 2021Article
- Diabetic retinopathy screening in urban primary care setting with a handheld smartphone-based retinal camera.Acta diabetologica · 2020Article
- Role of teleophthalmology to manage anterior segment conditions in vision centres of south India: EyeSmart study-I.Indian journal of ophthalmology · 2020Article
- Diabetic retinopathy screening: Telemedicine, the way to go!Indian journal of ophthalmology · 2018Article
- Embedded deep learning in ophthalmology: making ophthalmic imaging smarter.Therapeutic advances in ophthalmologyReview
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6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of this study is to analyze the yield of retinal images obtained in a rural diabetes eye care model.
methodsAn analysis of a sample of nonmydriatic fundus photography (NMFP) of posterior segment ophthalmic images, obtained by an indigenous equipment (3 nethra-Forus Royal), was done in a district-wide rural diabetic retinopathy (DR) screening program; a trained optometrist did the initial image grading. DR and diabetic macular edema (DME) were classified based on international DR and DME severity scale. The agreement between the optometrist and retina specialist was very good (κ = 0.932; standard error = 0.030; 95% confidence interval = 0.874-0.991).
resultsPosterior segment images of 2000 eyes of 1000 people with diabetes mellitus (DM) were graded. The mean age of the participants was 55.7 ± 11.5 standard deviation years. Nearly 42% of the screened participants (n = 420/1000) needed referral. The most common referable posterior segment abnormality was DR (8.2%). The proportion of people with any form of DR was seen in 110/1225 eyes, and sight-threatening DR was seen in 35/1225 eyes. About 62% of posterior segment images were gradable. The reasons for ungradable posterior segment images (34%) were small pupil, unfocused/partially available field of images, and cataract.
conclusionA NMFP model was able to detect referable posterior segment abnormalities in a rural diabetes eye care program. Reasons found for ungradability of images in the present study can be addressed while designing future DR screening programs in the rural areas.
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