Explore the vast range of titles available.

Diabetes is a global eye health issue. Given the rising in diabetes prevalence and ageing population, this poses significant challenge to perform diabetic retinopathy (DR) screening for these patients. Artificial intelligence (AI) using machine learning and deep learning have been adopted by various groups to develop automated DR detection algorithms. This article aims to describe the state-of-art AI DR screening technologies that have been described in the literature, some of which are already commercially available. All these technologies were designed using different training datasets and technical methodologies. Although many groups have published robust diagnostic performance of the AI algorithms for DR screening, future research is required to address several challenges, for examples medicolegal implications, ethics, and clinical deployment model in order to expedite the translation of these novel technologies into the healthcare setting.

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). Methods Retinal images from 758 patients with diabetes were collected during screening from various clinics in Poland. Each patient was graded by three graders with 320 patients graded by Polish and 438 patients graded by Indian graders, with the majority decision serving as the reference standard. The images were evaluated independently by the IDx-DR and RetCAD algorithms. Sensitivity, specificity, positive and negative predictive values, and agreement between algorithms and human graders were calculated and statistically compared. Results IDx-DR demonstrated higher sensitivity of 99.3% but lower specificity of 68.9% for RDR detection compared to RetCAD which had 89.4% sensitivity and 94.8% specificity. The positive predictive value was higher for RetCAD (96.4% vs 48.1% for IDx-DR) while the negative predictive value was higher for IDx-DR (99.5% vs 83.1% for RetCAD). Both algorithms achieved high sensitivity (> 95%) for sight-threatening diabetic retinopathy detection. Conclusion In this direct comparison using the same patient cohort, the two algorithms showed differences in their operating parameters for RDR screening. IDx-DR prioritized avoiding false negatives over false positives while RetCAD maintained a more balanced trade-off. These results highlight the variable performance of current artificial intelligence screening solutions and suggest the importance of considering algorithm performance metrics when deploying automated diabetic retinopathy screening programs, based on available healthcare resources.

Background: The prevalence of diabetic retinopathy (DR) is expected to increase. This will put an increasing strain on health care resources. Recently, artificial intelligence-based, autonomous DR screening systems have been developed. A direct comparison between different systems is often difficult and only two such comparisons have been published so far. As different screening solutions are now available commercially, with more in the pipeline, choosing a system is not a simple matter. Based on the images gathered in a local DR screening program we performed a retrospective comparison of IDx-DR and Retinalyze. Methods: We chose a non-representative sample of all referable DR positive screening subjects (n = 60) and a random selection of DR negative patient images (n = 110). Only subjects with four good quality, 45-degree field of view images, a macula-centered and disc-centered image from both eyes were chosen for comparison. The images were captured by a Topcon NW-400 fundus camera, without mydriasis. The images were previously graded by a single ophthalmologist. For the purpose of this comparison, we assumed two screening strategies for Retinalyze—where either one or two out of the four images needed to be marked positive by the system for an overall positive result at the patient level. Results: Percentage agreement with a single reader in DR positive and DR negative cases respectively was: 93.3%, 95.5% for IDx-DR; 89.7% and 71.8% for Retinalyze strategy 1; 74.1% and 93.6% for Retinalyze under strategy 2. Conclusions: Both systems were able to analyse the vast majority of images. Both systems were easy to set up and use. There were several limitations to the current pilot study, concerning sample choice and the reference grading that need to be addressed before attempting a more robust future study.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

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. Unanimous agreement between all graders was achieved for 385 eyes, and 110 patients, out of which 98% had a final grade of no DR. Thirty-six patients had final grades higher than mild DR, out of which only two had no grader disagreements regarding severity. A total of 28 eyes underwent adjudication due to complete grader disagreement. Four patients had discordant grades ranging from no DR to severe DR between the human graders and IDx-DR. Retina specialists achieved kappa scores of 0.52, 0.78, and 0.61. Retina specialists had relatively high grader variability and only a modest concordance with IDx-DR results. Focused training and verification are recommended for any potential DR graders before assessing DR screening images.

Antibiotic resistance in systemic infection is well-researched and well-publicized. Much less information is available on the resistance of normal ocular microbiome and that of ophthalmic infections. An understanding of the distribution of ocular microorganisms may help us in tailoring our empiric treatment, as well as in choosing effective pre-, peri- and postoperative management, to achieve the best results for patients. This study aims to summarize and review the available literature on the subject of normal ocular flora and its resistance, as well as the broader topic of antibiotic resistance in ophthalmology.