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. METHODS: In this retrospective-comparative study, retinal images with sufficient image quality were run on both ARIAs. They were captured in 811 consecutive patients with diabetes visiting diabetic clinics in Poland. For each patient, four non-mydriatic images, 45° field of view, i.e., two sets of one optic disc and one macula-centered image using Topcon NW400 were captured. Images were manually graded for severity of DR as no DR, any DR (mild non-proliferative diabetic retinopathy [NPDR] or more severe disease), RDR (moderate NPDR or more severe disease and/or clinically significant diabetic macular edema [CSDME]), or sight-threatening DR (severe NPDR or more severe disease and/or CSDME) by certified graders. The ARIA output was compared to manual consensus image grading (reference standard). RESULTS: On 807 patients, based on consensus grading, there was no evidence of DR in 543 patients (67%). Any DR was seen in 264 (33%) patients, of which 174 (22%) were RDR and 41 (5%) were sight-threatening DR. The sensitivity of detecting RDR against reference standard grading was 95% (95% CI: 91, 98%) and the specificity was 80% (95% CI: 77, 83%) for Medios AI. They were 99% (95% CI: 96, 100%) and 68% (95% CI: 64, 72%) for IDx-DR, respectively. CONCLUSION: Both the ARIAs achieved satisfactory accuracy, with few false negatives. Although false-positive results generate additional costs and workload, missed cases raise the most concern whenever automated screening is debated.

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.

Approval and Certification of Ophthalmic AI Devices in the European Union.

Artificial intelligence (AI)-based medical devices are already commercially available in Europe. The regulations surrounding the introduction and use of medical AI devices in the European Union (EU) are different to those in the USA, and the specifics of European legislature in medical AI are not commonly known. European law classifies medical devices into four classes: I, IIa, IIb, and III, depending on the perceived risk level of the device. Medical devices are certified under independent nongovernment bodies, and some can even self-certify their compliance with EU standards. The European "open" approach is vastly different from the strict perspective of the FDA, as reflected by the number of available medical AI devices. The EU is currently in a transitory period between two regulations, further complicating the legislative landscape. The devices in question deal with extremely sensitive data, collecting, processing, and sending images and diagnoses over the internet. The EU approach puts a large burden of verifying the effectiveness and integrity of the AI device on the consumer, without giving consumers many tools to do that effectively. This highlights the need for effective legislation and oversight from governing bodies, as well as the need for understanding the legalities and limitations of AI devices for those implementing them in clinical practice.

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. 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.

Analysis and Comparison of Two Artificial Intelligence Diabetic Retinopathy Screening Algorithms in a Pilot Study: IDx-DR and Retinalyze.

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.

Commonly used intracameral antibiotics for endophthalmitis prophylaxis: A literature review.

Endophthalmitis is a serious complication of cataract surgery that occurs in thousands of patients each year. To decrease the incidence of postoperative endophthalmitis, many surgeons inject intracameral antibiotics (cefuroxime, moxifloxacin, and vancomycin) routinely at the end of surgery. A large number of recently published retrospective studies and large database analyses have reported decreased endophthalmitis rates with routine antibiotic use, and the only prospective, multicenter, randomized trial performed by the European Society of Cataract and Refractive Surgery demonstrated that intracameral cefuroxime decreases the incidence of postoperative endophthalmitis. Routine cefuroxime use has become common in many European countries, whereas moxifloxacin is the most commonly used drug in India, and vancomycin use predominates in Australia. The decision regarding whether or not to use intracameral prophylaxis and the drug that is selected varies considerably throughout the world because of antibiotic availability and cost, and the spectrum of causative organisms. Adverse events due to intracameral antibiotics are infrequent, but complications such as hemorrhagic occlusive retinal vasculitis have been reported. Because additional prospective, comparative trials have not been performed, a consensus regarding best practices to prevent postoperative endophthalmitis has not been reached. Additionally, many surgeons do not routinely use intracameral antibiotics because they believe them unnecessary with modern aseptic techniques, small incision surgery, and shorter operating times. We discuss the most commonly used intracameral antibiotics, present the risks and potential benefits of this approach, and highlight challenges with drug compounding and safety.

Correction to: Artificial intelligence for diabetic retinopathy screening: a review.

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

Artificial intelligence for diabetic retinopathy screening: a review.

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.

Microbial flora and resistance in ophthalmology: a review.

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.