Revolutionizing retinal disease screening using “intelligent” smartphones

Abstract

AbstractDiabetic retinopathy (DR) and Glaucoma are among the leading causes of irreversible blindness globally. There are over 450 million Diabetics with 1 in 3 developing DR and 70 million affected with glaucoma. With the aging population the numbers are projected to increase exponentially. Early detection of DR with active screening is the key to prevent blindness secondary to advanced disease. Chronic glaucoma is frequently asymptomatic until central vision is impaired in advanced stages, resulting in a high proportion of undetected disease. Current teleophthalmology‐based screening solutions prevent scale up due to several challenges such as expensive equipment, delay in reporting and manual labour‐intensive grading process.The need of the hour for screening programs is a device that is portable, reliable, affordable with integrated AI providing rapid results. Remidio Fundus‐ on‐ phone (FOP) is one such device that has offline artificial intelligence (AI) algorithms that screens for referable diabetic retinopathy (RDR) and Glaucoma without the need for internet and cloud‐based inferencing. It utilizes smartphone technology for capturing non‐mydriatic fundus images which can be operated by minimally trained technicians with an inbuilt patient management software, auto capture and internal fixation features to improve ease‐of‐use. Further, it has the versatility to be used either as a handheld device, mounted on a slit lamp or in a stabilized version with chinrest, thus providing several options for the user. This smartphone‐based fundus camera has proven to be on par with high‐end desktop systems in terms of image quality in multiple published studies.The performance of the RDR AI has been clinically validated in multiple prospective trials. In three published studies conducted at diabetes care centres as well as in the community in India, the sensitivity ranged between 93%–100% with a specificity of 86.7%–92.5% for RDR. Additionally, it was found that the sensitivity to detect sight‐threatening disease which has a high risk of blindness, ranged between 98.7% to 100%. The AI has also been tested in several other populations in Armenia, Poland, Mexico and Caribbean islands with similar results. The novel Referable Glaucoma AI has similarly been prospectively validated against standard of care. Despite being challenged with fundus images alone the sensitivity to pick likely glaucoma was high with minimal overcall of normal subjects. Additionally, activation maps highlight areas of structural abnormality on the optic disc image, thus allowing visualization of key findings and better patient counselling.This device gives an opportunity to make a paradigm shift in the approach for screening as providers could get to the population to screen at‐risk individuals rather than relying on patients to come to clinics to be screened. It is thus an end‐to‐end solution for DR and Glaucoma screening combining two technological advances‐ smartphone technology and AI. With several upcoming algorithms to screen for other retinal conditions, this device is well positioned to be a gamechanger in the efforts aimed at reducing the burden of preventable retinal blindness across the world.