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

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.

Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine

“…Applications of AI and deep learning argued to be useful tools in assisting diagnosis and treatment decision making [10][11]. There were studies which promoted disease detection through AI models [12][13][14][15]. Use of mobile phones [16][17][18][19] and web based portals [20][21] have been tested successfully in health related data collection.…”

Application of machine learning in ophthalmic imaging modalities

“…The Google Chips and Amazon DeepLens cameras, allow embedding of advanced algorithms within devices, which is a useful approach in various medical fields [60]. Rajalakshmi et al combined an AI-based grading algorithm with a smartphone-based retinal imaging device for potential use in mass retinal screening of people with type 2 diabetes [61]. In 2018, IDx-DR was approved as the first fully autonomous AI-based DR diagnostic system by the United States Food and Drug Administration (FDA) [62]; this study is a milestone as the first prospective assessment of AI in the real-world.…”

Automated Smartphone Based System for Diagnosis of Diabetic Retinopathy

“…The challenge of low internet coverage can be solved using an independent smartphone application that is able to process a captured fundus image and automatically diagnose the stage of DR without any network connection. Rajalakshmi et al (2018) resulted in the highest performance by using a cloud-based diagnostic service while the fundus image is captured with a smartphone.…”