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

Abstract

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.

A practical model for effective eye care delivery in Southeast Asian rural communities: A proposal built based on experts’ recommendations

“…The experts believed that the biggest barriers to ensuring sustainable eye care delivery and positive patient outcomes in rural settings are retaining skilled surgeons,[ 11 ] lack of funding, infrastructure, patient awareness, and transportation. [ 12 ] Yet, sustainable eye care delivery requires empowering the local population, local eye care providers, optometrists,[ 13 ] and ophthalmologists[ 14 15 16 ] with the necessary education and skills required to address the root causes of vision impairment and blindness through primary care that is ultimately initiated and sustained by the rural communities themselves. Sustainable eye care delivery would thus prompt us to consider laying the foundation for community eye care through providing affordable and sustainable technology, and resources, upskilling rural eye care providers, and educating the rural populations such that they would be able to sustain quality eye care in the long run without requiring external aid.…”

Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis

“…In recent years, retrospective validation studies have shown AI to have high diagnostic accuracy in detecting DR [ 11 , 12 ]; that is, AI is equally good or even better than human graders (HGs). Studies done in real-world settings using prospective data collection have also demonstrated robust performance [ 13 ]; however, these studies are fewer than those done in a retrospective manner, and the true utility of AI systems in DR screening will only be better understood through prospective studies, as performance is likely to be affected when dealing with real-world data that is different from the data used for algorithm training [ 14 , 15 ]. Moreover, prospective studies, with pre-established protocols, allow them to be more robust and generalisable, and exhibit the true impact on system usability in real-world settings.…”