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Eye (Lond). 2018 Jun;32(6):1138-1144. doi: 10.1038/s41433-018-0064-9. Epub 2018 Mar 9.

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

Author information

1
Dr. Mohan's Diabetes Specialities Centre & Madras Diabetes Research Foundation, WHO Collaborating Centre for Noncommunicable Diseases Prevention and Control, IDF Centre of Excellence in Diabetes Care & ICMR Centre for Advanced Research on Diabetes, Chennai, Tamil Nadu, India. drraj@drmohans.com.
2
Dr. Mohan's Diabetes Specialities Centre & Madras Diabetes Research Foundation, WHO Collaborating Centre for Noncommunicable Diseases Prevention and Control, IDF Centre of Excellence in Diabetes Care & ICMR Centre for Advanced Research on Diabetes, Chennai, Tamil Nadu, India.

Abstract

OBJECTIVES:

To 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.

METHODS:

Three 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.

RESULTS:

Retinal 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.

CONCLUSIONS:

Automated 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.

PMID:
29520050
PMCID:
PMC5997766
DOI:
10.1038/s41433-018-0064-9
[Indexed for MEDLINE]
Free PMC Article

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