Capacity building for diabetic retinopathy screening by optometrists in India: Model description and pilot results

Examining the Role of Telemedicine in Diabetic Retinopathy

With the increasing prevalence of diabetic retinopathy (DR), screening is of the utmost importance to prevent vision loss for patients and reduce financial costs for the healthcare system. Unfortunately, it appears that the capacity of optometrists and ophthalmologists to adequately perform in-person screenings of DR will be insufficient within the coming years. Telemedicine offers the opportunity to expand access to screening while reducing the economic and temporal burden associated with current in-person protocols. The present literature review summarizes the latest developments in telemedicine for DR screening, considerations for stakeholders, barriers to implementation, and future directions in this area. As the role of telemedicine in DR screening continues to expand, further work will be necessary to continually optimize practices and improve long-term patient outcomes.

Determinants of the implementation of artificial intelligence-based screening for diabetic retinopathy-a cross-sectional study with general practitioners in Germany

Objective

Diabetic retinopathy (DR) may lead to irreversible damage to the eye and cause blindness if diagnosed in its advanced stages. Artificial intelligence (AI) may support screening and contribute to a timely diagnosis. The aim of this study was to evaluate factors that might influence the success of implementing AI-supported devices for DR screenings in general practice.

Methods

A questionnaire with modules on attitudes toward digital solutions, technical factors, perceived patient perspectives, and sociodemographic data was constructed and 2100 general practitioners (GPs) in Germany were invited to participate via a personal letter.

Results

Two hundred nine physicians participated in the survey (10% response rate, mean age = 54 years, 46% women). Acquisition costs (mean = 1.37), remuneration (mean = 1.46), and running costs (mean = 1.40) were considered particularly relevant in the context of AI-based screening tools. GPs indicated that a mean of €27.00 (SD = 19) was considered to be an appropriate reimbursement for an AI-based screening for DR in their practice. Less relevant factors were availability of a smartphone used in the practice (mean = 2.53) and time until the examination result was available (mean = 2.29). Important technical factors were practicability of the device (mean = 1.27), unproblematic installation of any necessary software (mean = 1.34), and the integrability into the practice information system (mean = 1.44). Considering the patient welfare, physicians rated the accuracy of the examination, omission of pupil dilation, and the duration of the examination as the most important factors. Participants ranked the factors broadening the scope of care, strengthening the primary care (PC) range, and signs of modern medical practice as the most important factors for making an AI-based screening tool attractive for their practice.

Conclusions

These findings serve as a basis for a successful implementation of AI-assisted screening devices in PC and might facilitate early screenings for ophthalmological diseases in general practice. The most relevant barriers that need to be overcome for a successful implementation of such tools include clarification of the costs and reimbursement policies.

Impact of targeted diabetic retinopathy training for graders in Vietnam and the implications for future diabetic retinopathy screening programmes: a diagnostic test accuracy study

OBJECTIVES

To compare the accuracy of trained level 1 diabetic retinopathy (DR) graders (nurses, endocrinologists and one general practitioner), level 2 graders (midlevel ophthalmologists) and level 3 graders (senior ophthalmologists) in Vietnam against a reference standard from the UK and assess the impact of supplementary targeted grader training.

DESIGN

Diagnostic test accuracy study.

SETTING

Secondary care hospitals in Southern Vietnam.

PARTICIPANTS

DR training was delivered to Vietnamese graders in February 2018 by National Health Service (NHS) UK graders. Two-field retinal images (412 patient images) were graded by 14 trained graders in Vietnam between August and October 2018 and then regraded retrospectively by an NHS-certified reference standard UK optometrist (phase I). Further DR training based on phase I results was delivered to graders in November 2019. After training, a randomised subset of images from January to October 2020 (115 patient images) was graded by six of the original cohort (phase II). The reference grader regraded all images from phase I and II retrospectively in masked fashion.

PRIMARY AND SECONDARY OUTCOME MEASURES

Sensitivity was calculated at the two different time points, and χ2 was used to test significance.

RESULTS

In phase I, the sensitivity for detecting any DR for all grader groups in Vietnam was low (41.8-42.2%) and improved in phase II after additional training was delivered (51.3-87.2%). The greatest improvement was seen among level 1 graders (p<0.001), and the lowest improvement was observed among level 3 graders (p=0.326). There was a statistically significant improvement in sensitivity for detecting referable DR and referable diabetic macular oedema between all grader levels. The post-training values ranged from 40.0 to 61.5% (including ungradable images) and 55.6%-90.0% (excluding ungradable images).

CONCLUSIONS

This study demonstrates that targeted training interventions can improve accuracy of DR grading. These findings have important implications for improving service delivery in DR screening programmes in low-resource settings.

Deep long and short term memory based Red Fox optimization algorithm for diabetic retinopathy detection and classification

Because of retina abnormalities of diabetic patients, the most common vision-threatening disease is diabetic retinopathy (DR). The DR diagnosis and prevention are challenging tasks as they may lead to vision loss. According to the literature analysis, the shortcomings in existing studies, such as failed to reduce the feature dimension, higher execution time, and higher computational cost, unable to tune the hyper-parameters, such as a number of hidden layers and learning rate, more computational complexities, higher cost, and so forth, during DR classification. To tackle these problems, we proposed a deep long- and short-term memory (LSTM) in a neural network with Red Fox optimization (deep LSTM-RFO) algorithm for DR classification. The four major components involved in the proposed methods are image preprocessing, segmentation, feature extraction, and classification. At first, an adaptive histogram equalization and histogram equalization model performs the fundus image preprocessing, thereby neglecting the noise and improving the contrast level of an image. Next, an adaptive watershed segmentation model effectively segments the lesion region based on the optic disc color and size of hemorrhages. At the third stage, we have extracted statistical, intensity, color, and shape features. Finally, the single normal class with three abnormal classes such as mild non-proliferative diabetic retinopathy, moderate NPDR, and severe NPDR are accurately classified using the deep LSTM-RFO algorithm. Experimentally, the MESSIDOR, STARE, and DRIVE datasets are used for both training and validation. MATLAB software performs the implementation process with respect to various evaluation criteria used. However, the proposed method accomplished superior performance, such as 98.45% specificity, 96.78% sensitivity, 97.92% precision, 96.89% recall, and 97.93% F-score results in terms of DR classification than previous methods.

Training of nonophthalmologists in diabetic retinopathy screening

The burden of diabetes mellitus (DM) and diabetic retinopathy (DR) is at alarming proportions in India and around the globe. The number of people with DM in India is estimated to increase to over 134 million by 2045. Screening and early identification of sight-threatening DR are proven ways of reducing DR-related blindness. An ideal DR screening model should include personalized awareness, targeted screening, integrated follow-up reminders, and capacity building. The DR screening technology is slowly shifting from direct examination by an ophthalmologist to remote screening using retinal photographs, including telescreening and automated grading of retinal images using artificial intelligence. The ophthalmologist-to-patient ratio is poor in India, and there is an urban-rural divide. The possibility of screening all people with diabetes by ophthalmologists alone is a remote possibility. It is prudent to use the available nonophthalmologist workforce for DR screening in tandem with the technological advances. Capacity-building efforts are based on the principle of task sharing, which allows for the training of a variety of nonophthalmologists in DR screening techniques and technology. The nonophthalmologist human resources for health include physicians, optometrists, allied ophthalmic personnel, nurses, and pharmacists, among others. A concurrent augmentation of health infrastructure, conducive health policy, improved advocacy, and increased people's participation are necessary requirements for successful DR screening. This perspective looks at the characteristics of various nonophthalmologist DR screening models and their applicability in addressing DR-related blindness in India.

Various models for diabetic retinopathy screening that can be applied to India

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.

Situational analysis of diabetic retinopathy screening in India: How has it changed in the last three years?

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. 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.

Capacity building in screening and treatment of diabetic retinopathy in Asia-Pacific region

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 <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 injections, the trained nurses can do the injections effectively and safely, the rate of endophthalmitis ranges from 0.03 to 0.07%, comparable to ophthalmologists. However, laws and regulations, which are different among countries, are challenges and barriers for nonophthalmologists, particularly for nonphysicians, for both screening and treatment of DR. Even if nonphysicians or physicians who are nonophthalmologists are legally approved for these tasks, sustainability of the capacity is another important challenge, this may be achieved if the capacity building can be part of their career development. Patient acceptability is another important barrier for initiating care provided by nonophthalmologists, particularly in Asia. There are also collaborations between national eye institutes of high-income countries, nongovernment organizations, and local eye institutes to improve both the quality and quantity of ophthalmologists and retinal specialists in low-income countries in Asia. This approach may require more labor, cost, and time consuming than training nonophthalmologists.