Abstract
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.
Highlights
Comparison of diabetic retinopathy (DR) severity between autonomous Artificial Intelligence (AI)-based outputs from an Food and Drug Administration (FDA)-approved screening system and human retina specialists’ gradings from ultra-widefield (UWF) colour images
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
Correlation with patient characteristics We found weak correlations between best-corrected visual acuity (BCVA), diabetes duration and type, gender, history of myocardial infarction and stroke, insulin intake, arterial hypertension, hyperlipidaemia, glaucoma, and autonomous artificial intelligence (AI) grading (Spearman’s correlation ranging from −0.15 to 0.27) and 7F-mask grading (Spearman’s correlation ranging from −0.22 to 0.20) and moderate positive correlations between haemoglobin A1C (HbA1c) and autonomous AI and between HbA1c and 7F-mask grading
Summary
Comparison 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. 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). CONCLUSIONS: The autonomous AI-based DR examination demonstrates sufficient accuracy in diagnosing asymptomatic nonproliferative diabetic patients with referable DR even compared to UWF imaging evaluated by human experts offering a suitable method for DR screening. IDx-DR was the first autonomous artificial intelligence (AI)-based diagnostic system approved by the U.S Food and Drug Administration (FDA) It consists of a robotic fundus camera and two types of algorithms, namely for image quality assessment as well as immediate diagnosis of the DR stage in case of sufficient image quality from four colour fundus images. IDx-DR was validated against the ETDRS protocol prognostic standard, and showed 87.2% sensitivity and 90.7% specificity for identifying ETDRS 35 and above, or any form of macular oedema, which includes moderate and vision-threatening DR that require consultation of an ophthalmologist [13, 14]
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