Recently updated global diabetic retinopathy screening guidelines: commonalities, differences, and future possibilities.
Taraprasad DasBrijesh TakkarSobha SivaprasadThamarangsi ThanksphonHugh TaylorPeter WiedemannJanos NemethPatanjali D NayarPadmaja Kumari RaniRajiv KhandekarPublished in: Eye (London, England) (2021)
Diabetic retinopathy (DR) is a global health burden. Screening for sight-threatening DR (STDR) is the first cost-effective step to decrease this burden. We analyzed the similarities and variations between the recent country-specific and the International Council of Ophthalmology (ICO) DR guideline to identify gaps and suggest possible solutions for future universal screening. We selected six representative national DR guidelines, one from each World Health Organization region, including Canada (North America), England (Europe), India (South- East Asia), Kenya (Africa), New Zealand (Western Pacific), and American Academy of Ophthalmology Preferred Practice Pattern (used in Latin America and East Mediterranean). We weighed the newer camera and artificial intelligence (AI) technology against the traditional screening methodologies. All guidelines agree that screening for DR and STDR in people with diabetes is currently led by an ophthalmologist; few engage non-ophthalmologists. Significant variations exist in the screening location and referral timelines. Screening with digital fundus photography has largely replaced traditional slit-lamp examination and ophthalmoscopy. The use of mydriatic digital 2-or 4-field fundus photography is the current norm; there is increasing interest in using non-mydriatic fundus cameras. The use of automated DR grading and tele-screening is currently sparse. Country-specific guidelines are necessary to align with national priorities and human resources. International guidelines such as the ICO DR guidelines remain useful in countries where no guidelines exist. Validation studies on AI and tele-screening call for urgent policy decisions to integrate DR screening into universal health coverage to reduce this global public health burden.
Keyphrases
- diabetic retinopathy
- artificial intelligence
- public health
- machine learning
- clinical practice
- healthcare
- primary care
- type diabetes
- editorial comment
- optical coherence tomography
- adipose tissue
- deep learning
- cardiovascular disease
- mental health
- mass spectrometry
- risk assessment
- current status
- social media
- induced pluripotent stem cells
- affordable care act
- single cell
- convolutional neural network