Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence

Ramachandran Rajalakshmi,Radhakrishnan Subashini,Ranjit Mohan Anjana,Viswanathan Mohan

doi:10.1038/s41433-018-0064-9

Ramachandran Rajalakshmi, Radhakrishnan Subashini + Show 2 more

Open Access

https://doi.org/10.1038/s41433-018-0064-9

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Abstract

ObjectivesTo assess the role of artificial intelligence (AI)-based automated software for detection of diabetic retinopathy (DR) and sight-threatening DR (STDR) by fundus photography taken using a smartphone-based device and validate it against ophthalmologist’s grading.MethodsThree hundred and one patients with type 2 diabetes underwent retinal photography with Remidio ‘Fundus on phone’ (FOP), a smartphone-based device, at a tertiary care diabetes centre in India. Grading of DR was performed by the ophthalmologists using International Clinical DR (ICDR) classification scale. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The retinal photographs were graded using a validated AI DR screening software (EyeArtTM) designed to identify DR, referable DR (moderate non-proliferative DR or worse and/or DME) or STDR. The sensitivity and specificity of automated grading were assessed and validated against the ophthalmologists’ grading.ResultsRetinal images of 296 patients were graded. DR was detected by the ophthalmologists in 191 (64.5%) and by the AI software in 203 (68.6%) patients while STDR was detected in 112 (37.8%) and 146 (49.3%) patients, respectively. The AI software showed 95.8% (95% CI 92.9–98.7) sensitivity and 80.2% (95% CI 72.6–87.8) specificity for detecting any DR and 99.1% (95% CI 95.1–99.9) sensitivity and 80.4% (95% CI 73.9–85.9) specificity in detecting STDR with a kappa agreement of k = 0.78 (p < 0.001) and k = 0.75 (p < 0.001), respectively.ConclusionsAutomated AI analysis of FOP smartphone retinal imaging has very high sensitivity for detecting DR and STDR and thus can be an initial tool for mass retinal screening in people with diabetes.

Highlights

All individuals with diabetes, irrespective of the type of diabetes, require regular and repetitive annual retinal screening for early detection and timely treatment of diabetic retinopathy (DR), sight-threatening diabetic retinopathy (STDR) [1, 2]
The Positive predictive value (PPV) for detection of DR, STDR and referable DR (RDR) was 89.7%, 75.3% and 74.6%, respectively
A high sensitivity for detection of DR, STDR and RDR using the EyeArt software when used on retinal images taken with Fundus on phone’ (FOP)

Summary

Introduction

Irrespective of the type of diabetes, require regular and repetitive annual retinal screening for early detection and timely treatment of diabetic retinopathy (DR), sight-threatening diabetic retinopathy (STDR) [1, 2]. Given the alarming increase in the number of people with diabetes and shortage of trained retinal specialists and graders of retinal photographs, an automated approach involving a computer-based analysis of the fundus images would reduce the burden of the health systems in screening for DR [6,7,8]. There is an increasing interest in the recent past in the development of automated analysis software using computer machine learning/artificial intelligence (AI)/deep neuronal learning for analysis of retinal images in people with diabetes [7, 9]. This paper studies the accuracy of the automated DR software in DR detection and screening for STDR using a previously validated smartphone-based retinal imaging system

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