Clinical Evidences

EyeArt REVERE 100,000 Patient Study

The large scale REtrospective Validation of Eyeart in the REal world (REVERE) study assessed the diagnostic efficacy of the EyeArt system screening for referable diabetic retinopathy in 107,001 consecutive diabetic patient visits. DEFINITIONS: DR: diabetic retinopathy NPDR: non-proliferative DR ICDR scale: International Clinical Diabetic Retinopathy severity scale with increasing levels of disease severity: No DR,

Artificial Intelligence Screening for Diabetic Retinopathy: Analysis from a Pivotal Multi-center Prospective Clinical Trial

Purpose Evaluate an artificial intelligence (AI) system to screen people with diabetes at point-of-care for diabetic retinopathy (DR) including diabetic macular edema (DME). Methods We conducted a prospective multi-center study (NCT03112005) in which patients with diabetes were enrolled consecutively initially and later preferentially based on enrichment criteria. The study subjects underwent undilated 2-field fundus photography

EyeArt UK NHS 20K Patients Study

Tufail conducted an independent, observational study of 20,258 consecutive patient visits comparing three automated DR screening systems: EyeArt, Retmarker DR, and iGrading. STUDY CONCLUSIONS: Compared to quality assured manual grading, EyeArt v1.0 achieves Acceptable sensitivity for referable DR Sufficient specificity to make it a cost effective alternative Sensitivity and false positive rates for EyeArt

Automated image analysis for Diabetic Retinopathy Screening with iPhone-based fundus camera

Purpose : Vision loss from diabetic retinopathy (DR) in ever-increasing population of diabetic patients can be prevented by early screening and diagnosis. To meet this growing need for screening, we present a cost-effective, end-to-end, point-of-care DR screening setup comprising a) iPhone based retinal camera, Ocular Cellscope, and b) analysis software for automated DR screening Methods : The

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

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

Advanced Retinal Image Analysis for AMD Screening Applications

Purpose Age-related macular degeneration (AMD) is a progressive eye condition that results in loss of central vision and severely impacts quality of life for over 15 million elderly Americans. Grading of primary form of AMD (dry AMD) is done by quantifying area of drusen bodies, a process that is slow and error prone when done

Automated Longitudinal Analysis of Lesion Changes in Retinal Images

Abstract Diabetic retinopathy (DR) is the leading cause of vision loss in the working age adults. With an increasing diabetic population there is an urgent need for smarter screening for DR and tracking its progress. Longitudinal analysis of fundus images can potentially be useful to evaluate changes in lesions and therefore helpful in assessing risk

Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis

Background: Diabetic retinopathy (DR)—a common complication of diabetes—is the leading cause of vision loss among the working-age population in the western world. DR is largely asymptomatic, but if detected at early stages the progression to vision loss can be significantly slowed. With the increasing diabetic population there is an urgent need for automated DR screening

Automated Detection of Diabetic Retinopathy Lesions on Ultrawidefield Pseudocolour Images

Purpose: We examined the sensitivity and specificity of an automated algorithm for detecting referral-warranted diabetic retinopathy (DR) on Optos ultrawide-field (UWF) pseudocolour images. Methods: Patients with diabetes were recruited for UWF imaging. A total of 383 subjects (754 eyes) were enrolled. Nonproliferative DR graded to be moderate or higher on the 5-level International Clinical Diabetic

Clinical Validation of Diabetic Retinopathy Lesion Segmentation in Ultra-Widefield Images

Purpose : Ultra-widefield (UWF) scanning laser ophthalmoscopy (SLO) imaging is a promising modality for diabetic retinopathy (DR). Manual segmentation of lesions on UWF images can be labor-intensive and error-prone. Therefore, there is a need for an automated lesion segmentation tool for UWF images. Methods : Our novel UWF retinal image analysis framework comprises of the following steps: