Evaluating the efficacy of AI systems in diabetic retinopathy detection: A comparative analysis of Mona DR and IDx-DR.

Purpose: To compare two artificial intelligence (AI)-based Automated Diabetic Retinopathy Image Assessment (ARIA) softwares in terms of concordance with specialist human graders and referable diabetic retinopathy (DR) diagnostic capacity.

Methods: Retrospective comparative study including 750 consecutive diabetes mellitus patients imaged for non-mydriatic fundus photographs. For each patient four images (45 degrees field of view) were captured, centered on the optic disc and macula.

Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Software: IDx-DR and RetCAD.

Introduction: Automated diabetic retinopathy (DR) screening using artificial intelligence has the potential to improve access to eye care by enabling large-scale screening. However, little is known about differences in real-world performance between available algorithms. This study compares the diagnostic accuracy of two AI screening platforms, IDx-DR and RetCAD, for detecting referable diabetic retinopathy (RDR).

Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence.

Introduction: Numerous studies have demonstrated the use of artificial intelligence (AI) for early detection of referable diabetic retinopathy (RDR). A direct comparison of these multiple automated diabetic retinopathy (DR) image assessment softwares (ARIAs) is, however, challenging. We retrospectively compared the performance of two modern ARIAs, IDx-DR and Medios AI.

A Global Review of Publicly Available Datasets Containing Fundus Images: Characteristics, Barriers to Access, Usability, and Generalizability.

This article provides a comprehensive and up-to-date overview of the repositories that contain color fundus images. We analyzed them regarding availability and legality, presented the datasets' characteristics, and identified labeled and unlabeled image sets. This study aimed to complete all publicly available color fundus image datasets to create a central catalog of available color fundus image datasets.

Variability of Grading DR Screening Images among Non-Trained Retina Specialists.

Poland has never had a widespread diabetic retinopathy (DR) screening program and subsequently has no purpose-trained graders and no established grader training scheme. Herein, we compare the performance and variability of three retinal specialists with no additional DR grading training in assessing images from 335 real-life screening encounters and contrast their performance against IDx-DR, a US Food and Drug Administration (FDA) approved DR screening suite. A total of 1501 fundus images from 670 eyes were assessed by each grader with a final grade on a per-eye level.

Cyclic growth of hierarchical structures in the aluminum-silicate system.

Background: Biological structures grow spontaneously from a seed, using materials supplied by the environment. These structures are hierarchical, with the 'building blocks' on each level constructed from those on the lower level. To understand and model the processes that occur on many levels, and later construct them, is a difficult task.