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

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

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 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:

EyeArt vs 7-field ETDRS Study

Clinical Validation Study of an automated DR Screening System against 7-field ETDRS Stereoscopic Reference Standard PURPOSE AND BACKGROUND: Comprehensive clinical validation of an automated diabetic retinopathy (DR) screening system, EyeArt v2.0 for detecting referable diabetic eye disease (DED) (moderate nonproliferative DR (NPDR) or higher on the ICDR scale and/or surrogate markers for clinically significant macular

EyeArt UK NHS 20K Patients Study

Tufail et.al 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