| Literature DB >> 34708732 |
Kim Ramasamy1, Chitaranjan Mishra1, Naresh B Kannan1, P Namperumalsamy1, Sagnik Sen1.
Abstract
With ever-growing prevalence of diabetes mellitus and its most common microvascular complication diabetic retinopathy (DR) in Indian population, screening for DR early for prevention of development of vision-threatening stages of the disease is becoming increasingly important. Most of the programs in India for DR screening are opportunistic and a universal screening program does not exist. Globally, telemedicine programs have demonstrated accuracy in classification of DR into referable disease, as well as into stages, with accuracies reaching that of human graders, in a cost-effective manner and with sufficient patient satisfaction. In this major review, we have summarized the global experience of telemedicine in DR screening and the way ahead toward planning a national integrated DR screening program based on telemedicine.Entities:
Keywords: Artificial intelligence; diabetes mellitus; diabetic retinopathy; telemedicine; telescreening; vision threatening
Mesh:
Year: 2021 PMID: 34708732 PMCID: PMC8725153 DOI: 10.4103/ijo.IJO_1442_21
Source DB: PubMed Journal: Indian J Ophthalmol ISSN: 0301-4738 Impact factor: 1.848
WHO screening criteria
| Condition should be a significant health problem |
| There should be an accepted treatment for patients with the disease |
| Diagnosis and treatment facilities should be available |
| An early symptomatic or latent stage should be present |
| There should be a test or examination method |
| The test should be acceptable to the population |
| The natural history of the condition should be adequately understood |
| There should be an agreed policy on patient selection for treatment |
| Case finding including diagnosis and treatment should be cost effective |
| Case finding should be a continuous process |
Different settings for DR screening
| Opportunistic screening | Systematic screening |
|---|---|
| • During the regular visit of the diabetic patient to a health care professional | • Regular DR screening camps in the community |
Commercially available fundus cameras
| Table-top/traditional fundus cameras | Smartphone-based fundus cameras |
|---|---|
| The OphthalmicDocs Fundus | |
| The limitations of UWF imaging include high cost, limited portability, and need of good patient cooperation during imaging. Recently several smartphone-based imaging systems have come up, which are easier to use, need much lesser investment and most importantly, are portable and can be used easily by the patients |
Levels of validation as recommended by the ATA-OTSIG
| Levels of validation | Application |
|---|---|
| Category 1 | Screen for the presence or absence of greater than minimal DR |
| Category 2 | Screen for patients with and without VTDR |
| Category 3 | To identify ETDRS-defined levels of NPDR (mild, moderate, or severe), PDR (early and high risk), and DME |
| Category 4 | A system that has been shown to match or exceed the ability of ETDRS photographs to identify lesions of DR |
Established DR screening programs in Western countries
| ATA categories | ||||
|---|---|---|---|---|
|
| ||||
| 1 | 2 | 3 | 4 | |
| DR grading | No or minimal DR | No DR | No DR | No DR |
| Mild DR | Mild DR | |||
| Mild or moderate DR | Moderate DR | Moderate DR | ||
| More than minimal DR | Severe NPDR | Severe NPDR | ||
| Early PDR | Early PDR | |||
| Vision threatening DR or DME | High risk DR | High risk DR | ||
| DME | DME | |||
| Functions | Screening | Screening and risk stratification | Screening, risk stratification, treatment recommendation | Exceeds ETDRS seven field photos, Can replace ETDRS photos in programs |
| Programs | Ophdiat (Paris, France)[ | EyeCheck (Netherlands)[ | Joslin Vision Netwrok (Massachusetts, USA)[ | None |
| EyePacs (CA, USA)[ | NHS Diabetic Eye Screening program (UK)[ | University of Alberta, (Canada)[ | ||
| Digiscope (Maryland, USA)[ | ||||
Figure 1(a) Conventional teleophthalmology screening model. (b) Semiautomated AI based screening model. (c) Fully automated AI-based screening model