Diabetic Retinopathy (DR) is a widespread disease and a leading cause of preventable blindness in the adult working population.
In 2020, the number of adults worldwide with DR was estimated to be 103.12 million. In addition, there is a continuous increase in type II diabetes which is often accompanied by DR, with only 40% of diabetic patients obtaining their annual recommended screening.
Whilst this diabetic population continuously grows, ophthalmology is facing a workforce crisis, with the number of ophthalmologists retiring each year superior to the new entering the field, rendering impossible to meet the DR screening needs of diabetic and DR patients.

Therefore, there is a need to improve the screening of DR both for public health and economic benefit. Many other challenges need to be addressed among which the fact that many countries have poor resources for DR screening and the patient adherence is low.

But artificial intelligence (AI) can help in a robust way. Deep learning-based methods show high accuracy in screening and are more robust to human error. AI, together with telemedicine, can also help when it comes to increasing patient adherence.

Fundación Ver Salud, together with RetinAI and Pharmaceutical company Novartis, developed and evaluated a cloud-based screening tool that uses artificial intelligence (AI), the LuxIA algorithm, to detect DR from a single fundus image.
The study evaluated LuxIA on three datasets, achieving mean sensitivity and specificity, with 95% confidence interval.

In this way, clinicians have better access to automated retina screening methodology for diagnosis and treatment, aimed ultimately at reducing the incidence of blindness caused by DR.

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Translational Vision Science & Technology