Journal Article

Comparison of early diabetic retinopathy staging in asymptomatic patients between autonomous AI-based screening and human-graded ultra-widefield colour fundus images

Gerendas, Bianca S, Sedova Aleksandra, Schmidt-Erfurth, Ursula, Hajdu Dorottya, Pollreisz Andreas, Neschi Martina, Aschauer, Julia, Datlinger Felix, Steiner, Irene

2022

Overview

Learning ObjectivesUpon completion of this activity, participants will:Compare diabetic retinopathy (DR) severity scores of ophthalmologically asymptomatic people with diabetes between outputs from an artificial intelligence (AI)-based system and human-graded ultra-widefield (UWF) color fundus imaging, according to a clinical study.Compare manual 7F-mask gradings vs UWF full-field gradings and describe the correlation with patient characteristics, according to a clinical study.Describe clinical implications of the comparison between the DR severity scores of ophthalmologically asymptomatic people with diabetes outputs using outputs from an AI-based system and human-graded UWF color fundus imaging, according to a clinical study.Accreditation StatementsIn support of improving patient care, this activity has been planned and implemented by Medscape, LLC and Springer Nature. Medscape, LLC is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.Medscape, LLC designates this Journal-based CME activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to 1.0 MOC points in the American Board of Internal Medicine’s (ABIM) Maintenance of Certification (MOC) program. Participants will earn MOC points equivalent to the amount of CME credits claimed for the activity. It is the CME activity provider’s responsibility to submit participant completion information to ACCME for the purpose of granting ABIM MOC credit.All other clinicians completing this activity will be issued a certificate of participation. To participate in this journal CME activity: (1) review the learning objectives and author disclosures; (2) study the education content; (3) take the post-test with a 75% minimum passing score and complete the evaluation at www.medscape.org/journal/eye; (4) view/print certificate.Credit hours1.0Release date:Expiration date:Post-test link:https://www.medscape.org/eye/posttest964708Authors/Editors disclosure informationS.S. has disclosed the following relevant financial relationships: Served as consultant or advisor for Allergan, Inc.; Bayer HealthCare Pharmaceuticals; Boehringer Ingelheim Pharmaceuticals, Inc.; Heidelberg Pharma GmbH; Novartis Pharmaceuticals Corporation; Optos; Roche; Served as a speaker or a member of a speakers bureau for Allergan, Inc.; Bayer HealthCare Pharmaceuticals; Boehringer Ingelheim Pharmaceuticals, Inc.; Novartis Pharmaceuticals Corporation; Optos; Roche; Received research funding from Bayer HealthCare Pharmaceuticals; Boehringer Ingelheim Pharmaceuticals, Inc.; Novartis Pharmaceuticals Corporation; Optos; Is employed by or has an executive role as Data Monitoring Chair for Phase 2 study sponsored by Bayer HealthCare Pharmaceuticals; Scientific Committee Member of EyeBio Steering Committee for FOCUS sponsored by Novo Nordisk. Other: Trustee member for Macular Society Scientific/Research Advisory Committee Member for Sight UK, Retina UK, Macular Society.Journal CME author disclosure informationLaurie Barclay has disclosed no relevant financial relationships.IntroductionComparison of diabetic retinopathy (DR) severity between autonomous Artificial Intelligence (AI)-based outputs from an FDA-approved screening system and human retina specialists’ gradings from ultra-widefield (UWF) colour images.MethodsAsymptomatic diabetics without a previous diagnosis of DR were included in this prospective observational pilot study. Patients were imaged with autonomous AI (IDx-DR, Digital Diagnostics). For each eye, two 45° colour fundus images were analysed by a secure server-based AI algorithm. UWF colour fundus imaging was performed using Optomap (Daytona, Optos). The International Clinical DR severity score was assessed both on a 7-field area projection (7F-mask) according to the early treatment diabetic retinopathy study (ETDRS) and on the total gradable area (UWF full-field) up to the far periphery on UWF images.ResultsOf 54 patients included (n = 107 eyes), 32 were type 2 diabetics (11 females). Mean BCVA was 0.99 ± 0.25. Autonomous AI diagnosed 16 patients as negative, 28 for moderate DR and 10 for having a vision-threatening disease (severe DR, proliferative DR, diabetic macular oedema). Based on the 7F-mask grading with the eye with the worse grading defining the DR stage 23 patients were negative for DR, 11 showed mild, 19 moderate and 1 severe DR. When UWF full-field was analysed, 20 patients were negative for DR, while the number of mild, moderate and severe DR patients were 12, 21, and 1, respectively.ConclusionsThe autonomous AI-based DR examination demonstrates sufficient accuracy in diagnosing asymptomatic non-proliferative diabetic patients with referable DR even compared to UWF imaging evaluated by human experts offering a suitable method for DR screening.

• Publisher: Nature Publishing Group
• Subject: Accreditation / Artificial intelligence / Asymptomatic / Color vision / Diabetes / Diabetes mellitus / Diabetic retinopathy / Edema / Educational objectives / Eye / Health care / Participation / Patients / Pharmaceuticals / Retina / Retinopathy