Rosendahllassen8349

The introduction of optical coherence tomography angiography (OCTA) has granted a significant improvement in the assessment of patients with diabetes. In this review, we will provide a description of the prominent OCTA findings in diabetes. In detail, this imaging technology proved that both the retinal and choroidal circulation is affected in diabetic subjects. The recent employment of widefield technology and a three-dimensional (3D) visualization in OCTA imaging are also discussed.Diabetic macular edema (DME), being a frequent manifestation of DR, disrupts the retinal symmetry. This event is particularly triggered by vascular endothelial growth factors (VEGF). Intravitreal injections of anti-VEGFs have been the most practiced treatment but an expensive option. A major challenge associated with this treatment is determining an optimal treatment regimen and differentiating patients who do not respond to anti-VEGF. As it has a significant burden for both the patient and the health care providers if the patient is not responding, any clinically acceptable method to predict the treatment outcomes holds huge value in the efficient management of DME. In such situations, artificial intelligence (AI) or machine learning (ML)-based algorithms come useful as they can analyze past clinical details of the patients and help clinicians to predict the patient's response to an anti-VEGF agent. The work presented here attempts to review the literature that is available from the peer research community to discuss solutions provided by AI/ML methodologies to tackle challenges in DME management. Lastly, a possibility for using two different types of data has been proposed, which is believed to be the key differentiators as compared to the similar and recent contributions from the peer research community.Artificial intelligence (AI) has evolved over the last few years; its use in DR screening has been demonstrated in multiple evidences across the globe. However, there are concerns right from the data acquisition, bias in data, difficulty in comparing between different algorithm, challenges in machine learning, its application in different group of population, and human barrier to AI adoption in health care. There are also legal and ethical concerns related to AI. The tension between risks and concerns on one hand versus potential and opportunity on the other have driven a need for authorities to implement policies for AI in DR screening to address these issues. The policy makers should support and facilitate research and development of AI in healthcare, but at the same time, it has to be ensured that the use of AI in healthcare aligns with recognized standards of safety, efficacy, and equity. It is essential to ensure that algorithms, datasets, and decisions are auditable and when applied to medical care (such as screening, diagnosis, or treatment) are clinically validated and explainable. Policy frameworks should require design of AI systems in health care that are informed by real-world workflow and human-centric design. Lastly, it should be ensured that healthcare AI solutions align with all relevant ethical obligations, from design to development to use and to be delivered properly in the real world.Vision-threatening diabetic retinopathy (VTDR) is one of the leading causes of impaired vision in the working-age population. Early identification, timely diagnosis, and prompt treatment of VTDR have to be tackled simultaneously to reduce the rate of blindness due to this condition. Considerable emphasis has been placed globally on establishing diabetic retinopathy screening (DRS) programs to enable early identification and referral of VTDR for treatment. However, there is an urgent need to shift from the common practice of opportunistic screening to a systematic DRS pathway to ensure that individuals with diabetes are screened at regular intervals and treated appropriately. While systematic DRS programs have been successfully established in countries such as the United Kingdom (UK), it continues to be a challenge to initiate and sustain such programs in low- and middle-income countries (LMIC), home to approximately 80% of people with diabetes. Telemedicine is widely recognized as an ideal DRS screening program. Although it has resulted in an upsurge of opportunistic screening, systematic recall of screened patients remains a challenge. In addition, the link between referred patients from the telemedicine programs to treatment centers is often not established or has failed to deliver; so, there is minimal impact of these telemedicine programs on VTDR blindness at present. This review covers the various barriers of establishing and sustaining systematic telemedicine DRS programs, especially in resource-constrained settings, and the challenges in aligning telemedicine to VTDR treatment pathways to ensure patients with VTDR are treated promptly and effectively.With ever-growing prevalence of diabetes mellitus and its most common microvascular complication diabetic retinopathy (DR) in Indian population, screening for DR early for prevention of development of vision-threatening stages of the disease is becoming increasingly important. Most of the programs in India for DR screening are opportunistic and a universal screening program does not exist. Globally, telemedicine programs have demonstrated accuracy in classification of DR into referable disease, as well as into stages, with accuracies reaching that of human graders, in a cost-effective manner and with sufficient patient satisfaction. In this major review, we have summarized the global experience of telemedicine in DR screening and the way ahead toward planning a national integrated DR screening program based on telemedicine.Diabetic retinopathy (DR) is a leading cause of blindness among adults and the numbers are projected to rise. There have been dramatic advances in the field of retinal imaging since the first fundus image was captured by Jackman and Webster in 1886. The currently available imaging modalities in the management of DR include fundus photography, fluorescein angiography, autofluorescence imaging, optical coherence tomography, optical coherence tomography angiography, and near-infrared reflectance imaging. These images are obtained using traditional fundus cameras, widefield fundus cameras, handheld fundus cameras, or smartphone-based fundus cameras. Fluorescence lifetime ophthalmoscopy, adaptive optics, multispectral and hyperspectral imaging, and multicolor imaging are the evolving technologies which are being researched for their potential applications in DR. Telemedicine has gained popularity in recent years as remote screening of DR has been made possible. Retinal imaging technologies integrated with artificial intelligence/deep-learning algorithms will likely be the way forward in the screening and grading of DR. We provide an overview of the current and upcoming imaging modalities which are relevant to the management of DR.The focus of capacity building for screening and treatment of diabetic retinopathy (DR) is on health professionals who are nonophthalmologists. Both physicians and nonphysicians are recruited for screening DR. Although there is no standardization of the course syllabus for the capacity building, it is generally accepted to keep their sensitivity >80%, specificity >95%, and clinical failure rate less then 5% for the nonophthalmologists, if possible. A systematic literature search was performed using the PubMed database and the following search terms diabetic retinopathy, diabetic retinopathy screening, Asia, diabetic retinopathy treatment, age-related macular degeneration, capacity building, deep learning, artificial intelligence (AI), nurse-led clinic, and intravitreal injection (IVI). AI may be a tool for improving their capacity. Capacity building on IVIs of antivascular endothelial growth factors for DR is focused on nurses. There is evidence that, after a supervision of an average of 100 initial injectio cost, and time consuming than training nonophthalmologists.The increased burden of diabetes in India has resulted in an increase in the complications of diabetes including sight-threatening diabetic retinopathy (DR). Visual impairment and blindness due to DR can be prevented by early detection and management of sight-threatening DR. Life-long evaluation by repetitive retinal screening of people with diabetes is an essential strategy as DR has an asymptomatic presentation. Fundus examination by trained ophthalmologists and fundus photography are established modes of screening. Various modes of opportunistic screening have been followed in India. Hospital-based screening (diabetes care/eye care) and community-based screening are the common modes. Tele-ophthalmology programs based on retinal imaging, remote interpretation, and grading of DR by trained graders/ophthalmologists have facilitated greater coverage of DR screening and enabled timely referral of those with sight-threatening DR. DR screening programs use nonmydriatic or mydriatic fundus cameras for retinal photography. Hand-held/smartphone-based fundus cameras that are portable, less expensive, and easy to use in remote places are gaining popularity. Good retinal image quality and accurate diagnosis play an important role in reducing unnecessary referrals. Recent advances like nonmydriatic ultrawide field fundus photography can be used for DR screening, though likely to be more expensive. The advent of artificial intelligence and deep learning has raised the possibility of automated detection of DR. Efforts to increase the awareness regarding DR is essential to ensure compliance to regular follow-up. Cost-effective sustainable models will ensure systematic nation-wide DR screening in the country.Of all the eye conditions in the contemporary Indian context, diabetic retinopathy (DR) attracts the maximum attention not just of the eye care fraternity but the entire medical fraternity. RG-6422 Countries are at different stages of evolution in structured DR screening services. In most low and middle income countries, screening is opportunistic, while in most of the high income countries structured population-based DR screening is the established norm. To reduce inequities in access, it is important that all persons with diabetes are provided equal access to DR screening and management services. Such programs have been proven to reverse the magnitude of vision-threatening diabetic retinopathy in countries like England and Scotland. DR screening should not be considered an endpoint in itself but the starting point in a continuum of services for effective management of DR services so that the risk of vision loss can be mitigated. Till recently all DR screening programs in India were opportunistic models where persons with diabetes visiting an eye care facility were screened. Since 2016, with support from International funders, demonstration models integrating DR screening services in the public health system were initiated. These pilots showed that a systematic integrated structured DR screening program is possible in India and need to be scaled up across the country. Many DR screening and referral initiatives have been adversely impacted by the COVID-19 pandemic and advocacy with the government is critical to facilitate continuous sustainable services.