Evaluation of Artificial Intelligence–Based Grading of Diabetic Retinopathy in Primary Care

Yogesan Kanagasingam; Di Xiao; Janardhan Vignarajan; Amita Preetham; Mei‐Ling Tay‐Kearney; Ateev Mehrotra

doi:10.1001/jamanetworkopen.2018.2665

Abstract

The results demonstrate both the potential and the challenges of using AI systems to identify diabetic retinopathy in clinical practice. Key challenges include the low incidence rate of disease and the related high false-positive rate as well as poor image quality. Further evaluations of AI systems in primary care are needed.

Keywords

MedicineDiabetic retinopathyFalse positive paradoxReferralGrading (engineering)RetinopathyClinical PracticeDiabetes mellitusDiseaseGold standard (test)Primary careOptometryPediatricsOphthalmologyInternal medicineArtificial intelligenceFamily medicineComputer science

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Publication Info

Year: 2018
Type: article
Volume: 1
Issue: 5
Pages: e182665-e182665
Citations: 182
Access: Closed

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APA Style

                            
                                
                                    Yogesan Kanagasingam, 
                                
                                    Di Xiao, 
                                
                                    Janardhan Vignarajan
                                
                                et al.
                            
                            (2018). 
                            Evaluation of Artificial Intelligence–Based Grading of Diabetic Retinopathy in Primary Care. 
                            JAMA Network Open
                            , 1
                            (5)
                            , e182665-e182665.
                            https://doi.org/10.1001/jamanetworkopen.2018.2665
                        

Identifiers

DOI: 10.1001/jamanetworkopen.2018.2665