P-161: Automated diabetic retinopathy detection in smartphone based fundus photography using artificial intelligence conducted at health camp in Assam
A RahaAbstract
Introduction
1 in 3 diabetics is at risk of developing diabetic retinopathy (DR). 1 in 10 Diabetics suffers vision loss. 80% of all vision impairment globally is avoidable.
Objectives
The study was conducted 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
1775 patients with type 2 diabetes underwent retinal photography with Remidio ‘Fundus on phone’ (FOP), a smartphone-based device, at health camp conducted at 5 places in the periphery of Lumding constituency of Assam. Out of 1775 patients, 501 patients were found to be having Diabetic Retinopathy of various grade. 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 501 patients were graded. DR was detected by the ophthalmologists in 323 (64.4%) and by the AI software in 343 (68.6%) patients while STDR was detected in 189 (37.8%) and 246 (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.
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 for early detection and timely referral to ophthalmologist.