Diagnostic Accuracy of Community-Based Diabetic Retinopathy Screening With an Offline Artificial Intelligence System on a Smartphone.

JAMA Ophthalmology
Sundaram NatarajanSobha Sivaprasad

Abstract

Offline automated analysis of retinal images on a smartphone may be a cost-effective and scalable method of screening for diabetic retinopathy; however, to our knowledge, assessment of such an artificial intelligence (AI) system is lacking. To evaluate the performance of Medios AI (Remidio), a proprietary, offline, smartphone-based, automated system of analysis of retinal images, to detect referable diabetic retinopathy (RDR) in images taken by a minimally trained health care worker with Remidio Non-Mydriatic Fundus on Phone, a smartphone-based, nonmydriatic retinal camera. Referable diabetic retinopathy is defined as any retinopathy more severe than mild diabetic retinopathy, with or without diabetic macular edema. This prospective, cross-sectional, population-based study took place from August 2018 to September 2018. Patients with diabetes mellitus who visited various dispensaries administered by the Municipal Corporation of Greater Mumbai in Mumbai, India, on a particular day were included. Three fields of the fundus (the posterior pole, nasal, and temporal fields) were photographed. The images were analyzed by an ophthalmologist and the AI system. To evaluate the sensitivity and specificity of the offline automated analysis...Continue Reading

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Citations

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