Observations and Lessons Learned From the Artificial Intelligence Studies for Diabetic Retinopathy Screening

2019 ◽  
Vol 137 (9) ◽  
pp. 994 ◽  
Author(s):  
Daniel Shu Wei Ting ◽  
Lawrence Carin ◽  
Michael D. Abramoff
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Lessons Learned ◽  
Diabetic Retinopathy Screening ◽  
Intelligence Studies ◽  
Retinopathy Screening

scholarly journals Correction to: Artificial intelligence for diabetic retinopathy screening: a review

Eye ◽  
2019 ◽  
Vol 34 (3) ◽  
pp. 604-604
Author(s):  
Andrzej Grzybowski ◽  
Piotr Brona ◽  
Gilbert Lim ◽  
Paisan Ruamviboonsuk ◽  
Gavin S. W. Tan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Diabetic Retinopathy Screening ◽  
Retinopathy Screening

scholarly journals Artificial intelligence for diabetic retinopathy screening: a review

Eye ◽  
2019 ◽  
Vol 34 (3) ◽  
pp. 451-460 ◽  
Author(s):  
Andrzej Grzybowski ◽  
Piotr Brona ◽  
Gilbert Lim ◽  
Paisan Ruamviboonsuk ◽  
Gavin S. W. Tan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Diabetic Retinopathy Screening ◽  
Retinopathy Screening

scholarly journals Analysis and Comparison of Two Artificial Intelligence Diabetic Retinopathy Screening Algorithms in a Pilot Study: IDx-DR and Retinalyze

2021 ◽  
Vol 10 (11) ◽  
pp. 2352
Author(s):  
Andrzej Grzybowski ◽  
Piotr Brona
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Pilot Study ◽  
Screening Program ◽  
Diabetic Retinopathy Screening ◽  
Retrospective Comparison ◽  
Screening Strategies ◽  
Set Up ◽  
Selection Of ◽  
Retinopathy Screening

Background: The prevalence of diabetic retinopathy (DR) is expected to increase. This will put an increasing strain on health care resources. Recently, artificial intelligence-based, autonomous DR screening systems have been developed. A direct comparison between different systems is often difficult and only two such comparisons have been published so far. As different screening solutions are now available commercially, with more in the pipeline, choosing a system is not a simple matter. Based on the images gathered in a local DR screening program we performed a retrospective comparison of IDx-DR and Retinalyze. Methods: We chose a non-representative sample of all referable DR positive screening subjects (n = 60) and a random selection of DR negative patient images (n = 110). Only subjects with four good quality, 45-degree field of view images, a macula-centered and disc-centered image from both eyes were chosen for comparison. The images were captured by a Topcon NW-400 fundus camera, without mydriasis. The images were previously graded by a single ophthalmologist. For the purpose of this comparison, we assumed two screening strategies for Retinalyze—where either one or two out of the four images needed to be marked positive by the system for an overall positive result at the patient level. Results: Percentage agreement with a single reader in DR positive and DR negative cases respectively was: 93.3%, 95.5% for IDx-DR; 89.7% and 71.8% for Retinalyze strategy 1; 74.1% and 93.6% for Retinalyze under strategy 2. Conclusions: Both systems were able to analyse the vast majority of images. Both systems were easy to set up and use. There were several limitations to the current pilot study, concerning sample choice and the reference grading that need to be addressed before attempting a more robust future study.

scholarly journals Using artificial intelligence for diabetic retinopathy screening: Policy implications

2021 ◽  
Vol 69 (11) ◽  
pp. 2993
Author(s):  
Rajiv Raman ◽  
Debarati Dasgupta ◽  
Kim Ramasamy ◽  
Ronnie George ◽  
Viswanathan Mohan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Policy Implications ◽  
Diabetic Retinopathy Screening ◽  
Screening Policy ◽  
Retinopathy Screening
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