System for diabetic retinopathy offers reduced costs, faster treatment for patients.
In this study, two-field nonmydriatic photographs were obtained and submitted for the automated AI-based analysis. The eyes then were dilated and four-wide field stereoscopic mydriatic fundus photography was carried out, he explained.
The study population included 1,830 that met the inclusion criteria and ultimately 1,674 were evaluated by the EyeArt system. The cohort was comprised of 50.3% men (mean age, 53.9 years; 73.3% Caucasian; 76.9% type 2 diabetes).
The study found that of the 1,194 EyeArt referable DR-negative eyes, 1,180 were found by the Wisconsin Fundus Photograph Reading Center to be referable DR-negative and 14 to be referable DR-positive.
“In all of the 14 cases deemed to be DR-negative by the EyeArt system, the level of the DR was low,” Dr. Sadda pointed out.
Related: Applying AI in fundus images
Discussing the false-positive cases, those for which the reading center saw no evidence of referral-warranted DR, Dr. Sadda explained that most cases had either some DR or other non-DR ocular diseases.
“Overall, the sensitivity and specificity of the system were 95.5% and 86.5%, respectively, if the ungradable cases (12.5%) were dilated,” he said.
The sensitivity and specificity values with no dilation were 95.5% and 86.0%, respectively.
According to Dr. Sadda, the EyeArt AI eye screening system for DR has high sensitivity and specificity for referable versus nonreferable DR compared with the standardized, adjudicated ETDRS grading of four-wide field stereo images.
“The system meets the pre-determined eye level sensitivity and specificity endpoints,” he concluded. “The hope is that these systems will be useful tools to help our patients in telescreening programs.”
Srinivas R. Sadda, MD
E: [email protected]
This article is adapted from Dr. Sadda's presentation at the American Academy of Ophthalmology 2019 annual meeting. Dr. Sadda has received National Eye Institute Small Business Innovation Research and Small Business Technology transfer grants in collaboration with Eyenuk.
1. Tufail et al., Ophthalmology. 2016;124:343-51
2. M Bhaskaranand et al., Diabetes Technology & Therapeutics. Oct 2019; https://doi.org/10.1089/dia.2019.0164