Purpose Use adjudication to quantify errors in diabetic retinopathy (DR) grading based on
individual graders and majority decision, and to train an improved automated algorithm for …

Purpose To understand the impact of deep learning diabetic retinopathy (DR) algorithms on
physician readers in computer-assisted settings. Design Evaluation of diagnostic …

Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-
level accuracy. This study aims to validate one such algorithm on a large-scale clinical …

Diabetic retinopathy (DR) grading is crucial in determining the adequate treatment and
follow up of patient, but the screening process can be tiresome and prone to errors. Deep …

Importance There has been wide interest in using artificial intelligence (AI)–based grading of
retinal images to identify diabetic retinopathy, but such a system has never been deployed …

Topic Diagnostic performance of deep learning–based algorithms in screening patients with
diabetes for diabetic retinopathy (DR). The algorithms were compared with the current gold …

Objective With the increasing prevalence of diabetes, annual screening for diabetic
retinopathy (DR) by expert human grading of retinal images is challenging. Automated DR …

Importance More than 60 million people in India have diabetes and are at risk for diabetic
retinopathy (DR), a vision-threatening disease. Automated interpretation of retinal fundus …

OBJECTIVE With rising global prevalence of diabetic retinopathy (DR), automated DR
screening is needed for primary care settings. Two automated artificial intelligence (AI) …

Current guidelines recommend that individuals with diabetes receive yearly eye exams for
detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset …