Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India

Abstract

IMPORTANCE More than 60 million people in India have diabetes and are at risk for diabetic retinopathy (DR), a vision-threatening disease. Automated interpretation of retinal fundus photographs can help support and scale a robust screening program to detect DR. OBJECTIVE To prospectively validate the performance of an automated DR system across 2 sites in India. DESIGN, SETTING, AND PARTICIPANTS This prospective observational study was conducted at 2 eye care centers in India (Aravind Eye Hospital and Sankara Nethralaya) and included 3049 patients with diabetes.

Detection of t(9;22) Chromosome Translocation Using Deep Residual Neural Network

“…During recent years, in order to reduce the burden of karyotype analysis, many computer-based auto- image classification. Gulshan showed the ability of CNN to detect diabetic retinopathy [7]. Esteva and others showed the ability of CNN to detect skin cancer [8].…”

Patients’ perspectives on the barriers to referral after telescreening for diabetic retinopathy in communities

“…5 Unfortunately, the rate of early detection and treatment of DR in low-income and middleincome countries has remained very low; this is due to an increase in the population with diabetes, lack of eye care resources, and limited access to quality and affordable eye care. [6][7][8] Telemedicine is a promising tool to address this public health issue, and has the potential to facilitate more widespread and cost-effective DR screening. [9][10][11] Meanwhile, many successful telemedicine programmes Epidemiology/Health Services Research have been established in high-income countries such as Europe and USA, and the integration of DR telescreening into the healthcare system as a regular service has been promoted deliberately by the governments.…”

Introducing automated diabetic retinopathy systems: it’s not just about sensitivity and specificity

“…Gulshan et al [1] present data to demonstrate the feasibility of using an automated grading system to expand diabetic retinopathy (DR) screening programmes. The automated system was used to compare manual grading from two sites in India using 5762 images from 3049 individuals.…”